Stefan Bosse - Cloud-Based Automated Design and Additive Manufacturing: A Usage Data-Enabled Paradigm Shift

Concept Paper

Cloud-Based Automated Design and Additive

Manufacturing: A Usage Data-Enabled

Paradigm Shift

Dirk Lehmhus

1,†

, Thorsten Wuest

,†

, Stefan Wellsandt

3,†

, Stefan Bosse

1,4,†

Toshiya Kaihara

5,†

, Klaus-Dieter Thoben

3,6

and Matthias Busse

Received: 4 November 2015; Accepted: 16 December 2015; Published: 19 December 2015

Academic Editor: Vittorio M. N. Passaro

ISIS Sensorial Materials Scientiﬁc Centre, University of Bremen, Bibliothekstraße 1, 28359 Bremen, Germany;

dirk.lehmhus@uni-bremen.de (D.L.); sbosse@uni-bremen.de (S.B.)

Industrial and Management Systems Engineering Department, Benjamin M. Statler College of Engineering

and Mineral Resources, West Virginia University, 333-A Mineral Resource BLDG, Morgantown,

WV 26506, USA

BIBA—Bremer Institut für Produktion und Logistik GmbH, Hochschulring 20, Bremen 28359, Germany;

wel@biba.uni-bremen.de (S.W.); tho@biba.uni-bremen.de (K.-D.T.)

Faculty of Mathematics & Computer Science, University of Bremen, Robert Hooke Str. 5,

28359 Bremen, Germany

Kobe University, Graduate School of Systems Informatics, Department of Systems Sciences, 1-1 Rokkodai,

Nada, Kobe 657-8501, Japan; kaihara@kobe-u.ac.jp

Faculty of Production Engineering, University of Bremen, Badgasteiner Straße 1, 28359 Bremen, Germany

Fraunhofer Institute for Manufacturing Technology and Advanced Materials, Wiener Straße 12,

28359 Bremen, Germany; matthias.busse@ifam.fraunhofer.de

* Correspondence: thwuest@mail.wvu.edu; Tel.: +1-304-293-9439

† These authors contributed equally to this work.

Abstract:

Integration of sensors into various kinds of products and machines provides access to

in-depth usage information as basis for product optimization. Presently, this large potential for

more user-friendly and efﬁcient products is not being realized because (a) sensor integration and

thus usage information is not available on a large scale and (b) product optimization requires

considerable efforts in terms of manpower and adaptation of production equipment. However,

with the advent of cloud-based services and highly ﬂexible additive manufacturing techniques,

these obstacles are currently crumbling away at rapid pace. The present study explores the state

of the art in gathering and evaluating product usage and life cycle data, additive manufacturing

and sensor integration, automated design and cloud-based services in manufacturing. By joining

and extrapolating development trends in these areas, it delimits the foundations of a manufacturing

concept that will allow continuous and economically viable product optimization on a general, user

group or individual user level. This projection is checked against three different application scenarios,

each of which stresses different aspects of the underlying holistic concept. The following discussion

identiﬁes critical issues and research needs by adopting the relevant stakeholder perspectives.

Keywords:

sensor integration; PEID; PLM; additive manufacturing; cloud-based manufacturing;

engineering design; product customization; product design; product development; automated design

1. Introduction

Imagine there’s a product that starts collecting data from the moment that it’s being made—a

product that collects, then evaluates these data, and passes the results on into the cloud: information

Sensors 2015, 15, 32079–32122; doi:10.3390/s151229905 www.mdpi.com/journal/sensors

Sensors 2015, 15, 32079–32122

that describes its making and its everyday experiences. In the cloud, this evidence is matched with

what other individual products of identical type and make provide, and scrutinized on an item and a

type level. In the cloud, this information is processed to automatically adapt design and dimensioning

of coming product generations. But why wait until a coming generation? And, why, speciﬁcally, do so

if all the information needed is readily at hand to optimize the product not only for some general set of

requirements, as sophisticated as they may be, but for an individual customer, and an individual use

pattern? Why not allow the product to evolve, not from generation to generation, but from item to

item? And do so differently from individual customer to individual customer, too?

Figure 1 graphically represents the impact of this concept on product generations and diversity,

with continuous change replacing the major revisions (these are retained in the new approach on

lower level with e.g., feature addition as discriminator, leading to product groups with close internal

and more remote external relation) as well as the minor ones of a more common organization of the

process. Conventional production technology, speciﬁcally in mass production, is not matched easily

with this vision. Mass production typically relies on complex manufacturing equipment and thus high

investment costs to reduce part costs based on an economy of scales approach: Flexibility is sacriﬁced

for the sake of productivity. Tools are matched to products, causing adaptation of the latter to be costly.

What, then, if production processes provided boundless ﬂexibility, and changes to the product could

be realized virtually, as changes to its digital representation, and at virtually no cost?

Sensors 2015, 15, page–page

1. Introduction

Imagine there’s a product that starts collecting data from the moment that it’s being made—a

product that collects, then evaluates these data, and passes the results on into the cloud: information

that describes its making and its everyday experiences. In the cloud, this evidence is matched with

what other individual products of identical type and make provide, and scrutinized on an item and

a type level. In the cloud, this information is processed to automatically adapt design and

dimensioning of coming product generations. But why wait until a coming generation? And, why,

specifically, do so if all the information needed is readily at hand to optimize the product not only

for some general set of requirements, as sophisticated as they may be, but for an individual

customer, and an individual use pattern? Why not allow the product to evolve, not from generation

to generation, but from item to item? And do so differently from individual customer to individual

customer, too?

Figure 1 graphically represents the impact of this concept on product generations and diversity,

with continuous change replacing the major revisions (these are retained in the new approach on

lower level with e.g., feature addition as discriminator, leading to product groups with close internal

and more remote external relation) as well as the minor ones of a more common organization of the

process. Conventional production technology, specifically in mass production, is not matched easily

with this vision. Mass production typically relies on complex manufacturing equipment and thus

high investment costs to reduce part costs based on an economy of scales approach: Flexibility is

sacrificed for the sake of productivity. Tools are matched to products, causing adaptation of the

latter to be costly. What, then, if production processes provided boundless flexibility, and changes to

the product could be realized virtually, as changes to its digital representation, and at virtually no

cost?

Figure 1. Consequences of the basic concept visualized: From product generations to continuous

optimization through gathering and usage of life cycle data in conjunction with flexible production.

In a manufacturing environment of this kind, the making of the product would not be linked to

the physical site at which dedicated tools and machinery were kept, simply because no such tools

and machinery would be needed. Instead, availability of the digital product information alone

would enable countless manufacturing centers worldwide to create the product without lead-time.

This is why such approaches have been termed Direct Digital Manufacturing [1].

A global manufacturing environment like this could transform the way we make things. The

paradigm shifts implied are manifold: For one thing, as product development would not require

parallel development of production equipment, design and production could move further apart. At

Figure 1.

Consequences of the basic concept visualized: From product generations to continuous

optimization through gathering and usage of life cycle data in conjunction with ﬂexible production.

In a manufacturing environment of this kind, the making of the product would not be linked

to the physical site at which dedicated tools and machinery were kept, simply because no such tools

and machinery would be needed. Instead, availability of the digital product information alone would

enable countless manufacturing centers worldwide to create the product without lead-time. This is

why such approaches have been termed Direct Digital Manufacturing [1].

A global manufacturing environment like this could transform the way we make things.

The paradigm shifts implied are manifold: For one thing, as product development would not require

parallel development of production equipment, design and production could move further apart.

At the same time, and as described above, ﬂexibility in manufacturing could be used to optimize

products on a customer or customer group basis, and furthermore, to implement a continuous design

optimization. Practically this means nothing less than brushing aside the fundamental concept of

individual product generations. Since optimization needs a basis, a primary prerequisite is a usage

32080

Sensors 2015, 15, 32079–32122

data link allowing backﬂow of information from product to designer. Equally important is a legally

and technically secure access for the various potential producers and service providers to initial and

optimized product design and manufacturing information. This global access to (general product and

usage) data, software tools and ﬁnally manufacturing resources is the primary link of our scenario

to the concept of the cloud (please consider also Figure 8 in Section 3 in this respect). Cloud-based

manufacturing (CBM), sometimes also designated Cloud Manufacturing (CMfg), in general is currently

the object of intense study [

–

] Xu et al., to give an example, introduced cloud manufacturing via

the earlier-adopted concept of cloud computing, deﬁned by the National Institute of Standards and

Technology (NIST) as “a model for enabling ubiquitous, convenient, on-demand network access to

a shared pool of conﬁgurable computing resources (e.g., networks, servers, storage, applications,

and services) that can be rapidly provisioned and released with minimal management effort or

service provider interaction.” [

]. Cloud manufacturing extends Cloud Computing by including

production processes (scheduling, resource planning etc.) and related Cyber-Physical Systems (CPS)

as active units [

]. Another deﬁnition has been provided by Wu et al., who discuss whether CBM

is indeed the paradigm change as which it is currently being advertised. Their answer is in the

afﬁrmative: By reviewing the suggested deﬁnitions of the ﬁeld and adding their own perspective, they

manage to delimit CBM both from earlier concepts like ﬂexible and redistributable manufacturing

systems and intermediate development stages like web- and agent-based manufacturing (WBM,

ABM) [

]. A true CBM approach, in their eyes, needs to integrate “Infrastructure-as-a-Service

(IaaS), Platform-as-a-Service (PaaS), Hardware-as-a-Service (HaaS), and Software-as-a-Service (SaaS)”

elements and is distinguished from WBM and ABM by its capability of facilitating new business

models through such elements [

]. In further publications, Wu et al. have linked their studies to

additive manufacturing and looked at product design, too, thus extending the original term CBM to

cloud-based design and manufacturing (CBDM) [

]. Interestingly, however, the usage data feedback

is not considered a core feature of CBM in these deﬁnitions: Data from the usage or Middle-of-Life

(MoL) phase is included in some of the projections offered, though not in an automated fashion, but

solely by way of direct end user (customer) feedback and integration in the design process (customer

co-design). The practical implementation of such end user-centered feedback facilities is seen as major

research issue. Besides, as we will show later in case study 2 (Section 4.2), the notion of separated ABM

and CBM approaches brought forward by Wu et al. can be overcome [9,10].

All the aforementioned capabilities require sophisticated analysis of data as glue between the

various enablers on technological level. On a more generic level, Artiﬁcial Intelligence (AI), Machine

Learning (ML) and advanced ICT are already integrated in most areas of daily life, as well as in

industrial production and product development. In parallel, the value of information and data is

rapidly increasing—more so if they help in satisfying customer needs. Companies who understand

the needs of their customers and at the same time are capable of translating them in a timely

manner into new products and product enhancements, have a competitive advantage in the global

business environment.

This statement accepted, several equally important aspects are required to succeed in the future:

‚ Availability of information about the usage of individual product items.

‚ Capability to analyze and translate this information into technical requirements.

‚ Capability to map these requirements to product design (e.g., CAD model).

‚ Capability to economically manufacture the products designed accordingly.

‚ Capability to increasingly automate these steps to realize fast time-to-market.

That said, we may match our own perspective of what might be called “Usage Data-Enhanced

Cloud-based Design and Manufacturing” or UDE-CBDM to the requirements proposed in the literature

to distinguish cloud-based from conventional manufacturing. We have done so in Figure 2, contrasting

the set of eight requirements a CBDM approach has to meet according to Wu et al. [

] with two

additional requirements that together with the initial ones constitute the UDE-CBDM case.

32081

Sensors 2015, 15, 32079–32122

Sensors 2015, 15, page–page

That said, we may match our own perspective of what might be called “Usage Data-Enhanced

Cloud-based Design and Manufacturing” or UDE-CBDM to the requirements proposed in the

literature to distinguish cloud-based from conventional manufacturing. We have done so in Figure 2,

contrasting the set of eight requirements a CBDM approach has to meet according to Wu et al. [8]

with two additional requirements that together with the initial ones constitute the UDE-CBDM case.

Figure 2. Requirements or criteria which characterize a CBDM approach, and additional ones which

constitute its extension to Usage Data-enhanced (UDE)-CBDM.

The additions we suggest are defined as follows:

Requirement 9, PEID-based Usage Data Collection, requires that perceptive product usage data

should continuously be collected and stored in a cloud-based data repository.

Requirement 10, Design-as-a-Service (DaaS) Capability, requires that a cloud-based service and

the associated tools should be established that retrieves data from the aforementioned repository

and transforms it into an adaptation of the products original root design that reflects the usage

information contained in the data by improving product performance, economy or the like on

this basis.

The reader should note that these are minimum requirements constituting a UDE-CBDM

scenario. We have deliberately dispensed with including AM or automated design approaches on

this level—ultimately, we believe these techniques to be a necessity (in case of AM) or a logical

development (automated design)—but they can still be omitted in a formal definition, as solutions

that do not build on them can theoretically be envisaged.

In practical implementations of this framework, given that (consumer and professional)

products are often used in a rather unique way by the customers, the usage information obtained is

most likely on an item-level. Today’s research in Product Lifecycle Management (PLM) is focusing

on the rather complex problem of how to capture, organize and present information along the

complete lifecycle. In the vision of this paper, PLM is the conceptual foundation providing the

necessary information to the other processes, e.g., from usage to design and manufacturing. On a

technological level, combinations of products with so-called product-embedded information devices

(PEIDs) are required. PEIDs can be seen as smart sensor systems or cyber-physical systems (CPS,

which brings the topic of their integration into products down to the field of sensor integration and

material-integrated intelligent systems.

Once the information is obtained and analyzed, which is far from trivial [11,12], the design

and manufacturing of highly individualized products adds further complexity. Whereas

one-of-a-kind-production and similar concepts have been discussed for years, today’s advanced

tools incorporate the opportunity to automate many of the time-consuming steps involved.

Figure 2.

Requirements or criteria which characterize a CBDM approach, and additional ones which

constitute its extension to Usage Data-enhanced (UDE)-CBDM.

The additions we suggest are deﬁned as follows:

Requirement 9, PEID-based Usage Data Collection, requires that perceptive product usage data

should continuously be collected and stored in a cloud-based data repository.

Requirement 10, Design-as-a-Service (DaaS) Capability, requires that a cloud-based service and

the associated tools should be established that retrieves data from the aforementioned repository and

transforms it into an adaptation of the products original root design that reﬂects the usage information

contained in the data by improving product performance, economy or the like on this basis.

The reader should note that these are minimum requirements constituting a UDE-CBDM

scenario. We have deliberately dispensed with including AM or automated design approaches

on this level—ultimately, we believe these techniques to be a necessity (in case of AM) or a logical

development (automated design)—but they can still be omitted in a formal deﬁnition, as solutions

that do not build on them can theoretically be envisaged.

In practical implementations of this framework, given that (consumer and professional) products

are often used in a rather unique way by the customers, the usage information obtained is most likely

on an item-level. Today’s research in Product Lifecycle Management (PLM) is focusing on the rather

complex problem of how to capture, organize and present information along the complete lifecycle. In

the vision of this paper, PLM is the conceptual foundation providing the necessary information to the

other processes, e.g., from usage to design and manufacturing. On a technological level, combinations

of products with so-called product-embedded information devices (PEIDs) are required. PEIDs can be

seen as smart sensor systems or cyber-physical systems (CPS, which brings the topic of their integration

into products down to the ﬁeld of sensor integration and material-integrated intelligent systems.

Once the information is obtained and analyzed, which is far from trivial [

], the

design and manufacturing of highly individualized products adds further complexity. Whereas

one-of-a-kind-production and similar concepts have been discussed for years, today’s advanced

tools incorporate the opportunity to automate many of the time-consuming steps involved.

Knowledge-based engineering [

] and adaptive design [

] automate speciﬁc tasks in the engineering

domain while additive manufacturing gives designers and manufacturers more freedom to

realize products.

The only suitable manufacturing process class that may provide the ﬂexibility our vision requires

is additive manufacturing (AM), which is capable of producing a part directly from a digital model.

This way, product modiﬁcations can in principle be handled entirely in the virtual world, allowing

economic deﬁnition and production of individual product variants. However, this choice of a process

32082

Sensors 2015, 15, 32079–32122

class has implications for sensor integration, since there is the need to link both topics. Thus in our

vision, additive manufacturing is important in two different ways, ﬁrstly, regarding the aforementioned

degrees of freedom during production and, secondly, by facilitating the integration of the necessary

sensors to capture usage information. Additive manufacturing thus becomes the link that closes the

loop: The capturing of information during the usage phase is a prerequisite for the whole vision to

work, and sensors are required to obtain this information, even though complementary information

can be collected from other sources as well [15].

With this overall vision in mind, the different areas and their current state of the art are illustrated

in the second chapter. In its ﬁrst Section 2.1, product life cycle management (PLM) and item-level

information ﬂows, as well as Intelligent Products as one way of capturing information, are introduced.

Following this, in Section 2.2 the focus is put speciﬁcally on product usage information, based on its

key role in our concept, before the topic of sensor-integration in additive manufacturing is touched

upon in Section 2.3. Within it, additive manufacturing is described in greater detail, looking into the

current ability to create complex and multi-material structures. Section 2.4 explores how automated

design, based on PLM data, may be capable of creating customized products in an efﬁcient and timely

way. The synthesis of all elements contributing to our concept is explained in more detail, though

still on a level of principles, in Section 3. It is further illustrated via three comprehensive case studies,

which together form Section 4. The ﬁrst of these is focused on medical implants. In this application, the

generation-to-generation adaptation based on strictly individual use patterns is a challenge that links

up to our vision especially in terms of the need for customization. Besides, the application as such is

already associated to AM, which is an established manufacturing process in this ﬁeld, and to sensor

integration, though in the latter case mainly on an academic level. The second case addresses robots

as products that are integrated in a closed-loop adaptive design and manufacturing process. They

contribute their inherent connectivity and sensor integration to support the cloud-based manufacturing

vision. The third case represents a near-complete implementation study of our vision, using the shoe

industry as example. Except for an extensive automation of the usage data-driven design processes,

all elements of the present study’s underlying notions are reﬂected in this example.All case studies

are critically discussed in Section 5, alternately assuming the positions of the various stakeholders

involved. Section 6 concludes our paper and provides an outlook on further paths of research in

the area.

2. Fundamental Technologies and Concepts: A Literature Review

The vision we propose is covering an exceedingly broad spectrum of technologies, ranging from

Additive Manufacturing (AM) via Product Life Cycle Management (PLM) to existing concepts of

Cloud-based Design and Manufacturing (CBDM) and sensor integration. In the present section, the

state of the art of such enabling technologies is illustrated, providing a solid, common foundations

for our readers, whom we expect to come from several different backgrounds. The aim is to allow

all of them, irrespective from which research community they may stem, to acquire basic knowledge

in adjacent ﬁelds which will help them grasp the implications and potential of the framework we

propose, and follow the arguments in the case studies and discussion sections more easily. Note that

we have refrained from covering CBDM in this context, since we consider this the broader basis and

have already introduced it as such in Section 1.

2.1. Product Lifecycle Management (PLM)

PLM is the process of handling product data, information and knowledge across a product’s

lifecycle [

]. The terms “data” and “information” are used synonymously in the following for

simplicity’s sake. The product lifecycle is a concept lent from biology where it describes the recurring

change of states for certain organisms (see [

] for biological lifecycle). In engineering, the organisms

are exchanged for tangible goods (products) and the state changes are substituted by processes, such

as design, production, use, repair, recycling and disposal. Since lifecycle processes differ among the

32083

Sensors 2015, 15, 32079–32122

targeted products, they are generalized into three phases stated as “beginning of life” (BOL), “middle

of life” (MOL) and “end of life” (EOL) as illustrated in Figure 3. The BOL covers the design and the

realization of the product, the MOL concerns the product’s usage and related value-adding services,

while the EOL typically consists of several optional activities, such as reuse by other customers (second

hand), remanufacturing (refurbish used product), material recycling and disposal. Along the product

lifecycle, matter, energy and information is exchanged and transformed. Multiple internal and external

stakeholders are involved along the lifecycle of a product.

Sensors 2015, 15, page–page

2.1. Product Lifecycle Management (PLM)

PLM is the process of handling product data, information and knowledge across a product’s

lifecycle [16]. The terms “data” and “information” are used synonymously in the following for

simplicity’s sake. The product lifecycle is a concept lent from biology where it describes the

recurring change of states for certain organisms (see [17] for biological lifecycle). In engineering, the

organisms are exchanged for tangible goods (products) and the state changes are substituted by

processes, such as design, production, use, repair, recycling and disposal. Since lifecycle processes

differ among the targeted products, they are generalized into three phases stated as “beginning of

life” (BOL), “middle of life” (MOL) and “end of life” (EOL) as illustrated in Figure 3. The BOL covers

the design and the realization of the product, the MOL concerns the product’s usage and related

value-adding services, while the EOL typically consists of several optional activities, such as reuse

by other customers (second hand), remanufacturing (refurbish used product), material recycling and

disposal. Along the product lifecycle, matter, energy and information is exchanged and transformed.

Multiple internal and external stakeholders are involved along the lifecycle of a product.

Figure 3. Model of a product lifecycle and important information flows [18].

Traditional information in PLM focuses on product types rather than individual products,

examples are 3D models and parts lists. These data are called master data—i.e., data that is changing

seldom. An increasing amount of information along the lifecycle is related to the usage and service

of products. Typical for this information is that it describes specific items, i.e., identifiable, unique

products. Though there have been research projects in the past (e.g., [19,20]), the transition from

traditional PLM to an item-level perspective (i.e., item-level PLM) is still a grass-roots movement.

From a technical point of view, item-level PLM is conceptually related to Intelligent Products,

which are also known as Smart Products [21]. These products are one possible way to enable an

individual product to generate, store and communicate data throughout its lifecycle. Intelligent

Products are physical items, which may be transported, processed or used and which comprise the

ability to act in an intelligent (“smart”) manner of various degrees [22]. McFarlane et al. define the

Intelligent Product as “[...] a physical and information based representation of an item [...] which possesses a

unique identification, is capable of communicating effectively with its environment, can retain or store data

about itself, deploys a language to display its features, production requirements, etc., and is capable of

participating in or making decisions relevant to its own destiny” [22]. In order for intelligent products to

realize the aforementioned potential, the capabilities of sensing, storing, processing and communicating

information has to be implemented.

Product Embedded Information Devices (PEIDs) [23] describe a realization of the concept of

Intelligent Products. They act as embedded information gathering devices linked to sensors, which

are able to sense their environment and their condition and communicate the data gathered

wirelessly. PEIDs are categorized according to their capabilities with regards to data storage and

data processing [24]. In addition to these features, the devices’ ability to integrate sensors, as well as

their options for network connectivity are used to distinguish different types of PEIDs. Since PEIDs

Figure 3. Model of a product lifecycle and important information ﬂows [18].

Traditional information in PLM focuses on product types rather than individual products,

examples are 3D models and parts lists. These data are called master data—i.e., data that is changing

seldom. An increasing amount of information along the lifecycle is related to the usage and service

of products. Typical for this information is that it describes speciﬁc items, i.e., identiﬁable, unique

products. Though there have been research projects in the past (e.g., [

]), the transition from

traditional PLM to an item-level perspective (i.e., item-level PLM) is still a grass-roots movement.

From a technical point of view, item-level PLM is conceptually related to Intelligent Products,

which are also known as Smart Products [

]. These products are one possible way to enable an

individual product to generate, store and communicate data throughout its lifecycle. Intelligent

Products are physical items, which may be transported, processed or used and which comprise the

ability to act in an intelligent (“smart”) manner of various degrees [

]. McFarlane et al. deﬁne the

Intelligent Product as “[...] a physical and information based representation of an item [...] which possesses a

unique identiﬁcation, is capable of communicating effectively with its environment, can retain or store data about

itself, deploys a language to display its features, production requirements, etc., and is capable of participating

in or making decisions relevant to its own destiny” [

]. In order for intelligent products to realize

the aforementioned potential, the capabilities of sensing, storing, processing and communicating

information has to be implemented.

Product Embedded Information Devices (PEIDs) [

] describe a realization of the concept

of Intelligent Products. They act as embedded information gathering devices linked to sensors,

which are able to sense their environment and their condition and communicate the data gathered

wirelessly. PEIDs are categorized according to their capabilities with regards to data storage and data

processing [

]. In addition to these features, the devices’ ability to integrate sensors, as well as their

options for network connectivity are used to distinguish different types of PEIDs. Since PEIDs typically

contain a global, unique identiﬁer one of the most basic requirement towards mapping the generated

information to an individual product is met. Information delivered by PEIDs has to be integrated into

the overall PLM system [25].

32084

Sensors 2015, 15, 32079–32122

2.2. Product Usage Information

According to a working deﬁnition provided by Wellsandt et al., “usage information” is

“[ . . . ]

any product-related information that is created after the product is sold to the end customer and before

the product is no longer useful for a user.” [

]. There are different media providing access to usage

information, such as social media, maintenance reports, customer complaints hotlines and PEIDs [

Since this paper focuses on Intelligent Products, only PEIDs will be considered further. PEIDs measure

the current state of the product item, its environment and any relevant actors (e.g., the user). This

measured raw data has to be transformed into useful information. The transformation can be a complex

task, potentially involving statistics and pattern recognition. The resulting usage information describes

objects, procedures and events of the MOL phase. Other information sources might be necessary to

deliver contextual information in case the sensor data cannot be unambiguously understood.

In product design, information about the usage of products helps engineers to understand, for

instance, which product features are ﬁt for purpose and which ones are not. Usage information is

describing different activities of the usage phase. For durable goods with a long lifetime, the MOL

phase might consist of ﬁve distinct processes as illustrated in Figure 4.

Sensors 2015, 15, page–page

typically contain a global, unique identifier one of the most basic requirement towards mapping the

generated information to an individual product is met. Information delivered by PEIDs has to be

integrated into the overall PLM system [25].

2.2. Product Usage Information

According to a working definition provided by Wellsandt et al., “usage information” is “[…]

any product-related information that is created after the product is sold to the end customer and

before the product is no longer useful for a user.” [18]. There are different media providing access to

usage information, such as social media, maintenance reports, customer complaints hotlines and

PEIDs [26]. Since this paper focuses on Intelligent Products, only PEIDs will be considered further.

PEIDs measure the current state of the product item, its environment and any relevant actors (e.g.,

the user). This measured raw data has to be transformed into useful information. The transformation

can be a complex task, potentially involving statistics and pattern recognition. The resulting usage

information describes objects, procedures and events of the MOL phase. Other information sources

might be necessary to deliver contextual information in case the sensor data cannot be

unambiguously understood.

In product design, information about the usage of products helps engineers to understand, for

instance, which product features are fit for purpose and which ones are not. Usage information is

describing different activities of the usage phase. For durable goods with a long lifetime, the MOL

phase might consist of five distinct processes as illustrated in Figure 4.

Figure 4. Middle of life phase for durable goods (adapted from [27], see also [26]).

The figure concerns a single user scenario only. It consists of the actual usage activity and four

optional supplementary activities. Optional activities are can be relevant for some types of product

but not for others. The first process that is conducted in the MOL phase is the commission of the

delivered product item. During commission, the product is prepared for the actual usage by, for

instance, connecting it to local infrastructure, configuring its software and adjusting it to meet the

personal characteristics of the user. Commission can be done by the user or a service provider. The

following use activity provides the actual benefits of the product to its user. It can be interrupted by

maintenance activities aiming to preserve the functions of the product. In case the product is broken,

repair activities can be performed. When the product item is no longer useful for the user, it is

decommissioned. The decommissioning of the product can be conducted by the user or a service

provider. In case the commission and decommission activities are conducted by third parties, the

activities do not belong to the middle of life in a formal sense. The information gathered during these

activities might be, however, still considered usage information.

2.3. Additive Manufacturing Technology and State of the Art

Additive Manufacturing is the physical basis of our concept, which sinks or swims with the

almost boundless flexibility AM promises—and actually delivers in most respects. Nevertheless the

concept comprises requirements for features that surmount what the state of the art has to offer.

Primarily, this concerns the ability to easily include sensors, intelligent sensor systems or PEIDs in

the building job. The present chapter will approach these issues step by step, in the course providing

an overview of current AM capabilities.

Figure 4. Middle of life phase for durable goods (adapted from [27], see also [26]).

The ﬁgure concerns a single user scenario only. It consists of the actual usage activity and four

optional supplementary activities. Optional activities are can be relevant for some types of product but

not for others. The ﬁrst process that is conducted in the MOL phase is the commission of the delivered

product item. During commission, the product is prepared for the actual usage by, for instance,

connecting it to local infrastructure, conﬁguring its software and adjusting it to meet the personal

characteristics of the user. Commission can be done by the user or a service provider. The following

use activity provides the actual beneﬁts of the product to its user. It can be interrupted by maintenance

activities aiming to preserve the functions of the product. In case the product is broken, repair activities

can be performed. When the product item is no longer useful for the user, it is decommissioned.

The decommissioning of the product can be conducted by the user or a service provider. In case the

commission and decommission activities are conducted by third parties, the activities do not belong

to the middle of life in a formal sense. The information gathered during these activities might be,

however, still considered usage information.

2.3. Additive Manufacturing Technology and State of the Art

Additive Manufacturing is the physical basis of our concept, which sinks or swims with the almost

boundless ﬂexibility AM promises—and actually delivers in most respects. Nevertheless the concept

comprises requirements for features that surmount what the state of the art has to offer. Primarily, this

concerns the ability to easily include sensors, intelligent sensor systems or PEIDs in the building job.

The present chapter will approach these issues step by step, in the course providing an overview of

current AM capabilities.

32085

Sensors 2015, 15, 32079–32122

2.3.1. Processes and Business Cases for Conventional AM

The game-changing potential of Additive Manufacturing has been spelled out by Campbell et al.

in a recent foresight study: “Designs, not products, would move around the world as digital ﬁles to be

printed anywhere by any printer that can meet the design parameters. The Internet ﬁrst eliminated

distance as a factor in moving information and now AM eliminates it for the material world. Just as a

written document can be emailed as a PDF and printed in 2D, an ‘STL’ design ﬁle can be sent instantly

to the other side of the planet via the Internet and printed in 3D” [28].

As technological area, Additive Manufacturing (AM) bears a multitude of names, some of them

misleading. There is, however, a common deﬁnition that allows a clear distinction between what is in

fact AM, and what is not: According to ASTM international committee F42, Additive Manufacturing

is the “process of joining materials to make objects from 3D model data, usually layer upon layer, as

opposed to subtractive manufacturing methodologies” [29].

Table 1 provides a rough overview of available additive manufacturing processes and links

them to the material classes processed. Where process and material intersect, concrete examples

and references explaining them in more detail are provided. The list is not conclusive, but provides

examples and associated references only.

Table 1.

Examples of AM techniques available today, classiﬁed by basic principle according to ASTM

F2792-12a [29] and class of materials.

Basic Principle

Class of Material

Polymer/Organic Metal Ceramic

Binder jetting

3D printing

[30] 3D printing

[31,32]

Directed energy deposition

Laser Engineered Net

Shaping/LENS™ [33],

Directed Light

Fabrication/DLF [34],

Direct Metal

Deposition/DMD [35]

Laser Engineered Net

Shaping/LENS

[36]

Material extrusion

Fused Deposition Modelling,

FDM [37]

Fused Deposition

Modelling, FDM [38,39];

Multiphase Jet

Solidiﬁcation, MJS [40]

Fused Deposition Modelling,

FDM [41]; Robocasting [42];

Freeze-form Extrusion

Fabrication, FEF [43,44]

Material jetting

Direct Printing, DIP [45]; Multi-Jet

Modeling/MJM or Polyjet

Modeling/PJM [46].

Direct Printing, DIP [47] Direct Printing, DIP [32]

Powder bed fusion

Selective Laser Sintering, SLS

[48]

Selective Laser

Melting/SLM

[49];

Selective Laser Sintering,

SLS

[50]; Direct Metal

Laser Sintering, DMLS [51];

Electron Beam Additive

Manufacturing, EBAM [52]

Selective Laser

Sintering/SLS

[32,53]

Sheet Lamination

Laminated Object Manufacture,

LOM [54,55]

Laminated Object

Manufacture, LOM [56,57];

Plate Diffusion

Brazing/PDB [58]

Laminated Object

Manufacture, LOM [32]

Vat photo-polymerization Stereolithography/SLA [59] Stereolithography [60,61]

Stereolithography/SLA

[32,62–64]

This term is sometimes erroneously used as synonym for Additive Manufacturing in general;

SLM, SLS and DMLS very often describe almost identical processes.

The reader should note, though, that (a) this overview cannot cover the full scope of AM

techniques and that, (b), different designations are around for almost identical processes, as many

proprietary terms by equipment suppliers etc. have entered the literature. Our collection will give a ﬁrst

idea of the present diversity and direct the interested reader towards sources of further information.

As shown in Table 1, and based on the relevant ASTM terminology standard F2792-12a, the main

processes currently in use for Additive Manufacturing can roughly be sorted in seven categories:

32086

Sensors 2015, 15, 32079–32122

Binder jetting implies a bonding agent selectively deposited to join powder materials. Product

geometry is determined, layer by layer, via this deposition step, while the matrix is provided as

powder bed.

Directed energy deposition uses some deposition technique to furnish the matrix material and

deﬁnes part shape in parallel. Building material typically comes in particulate form, and coherence is

achieved by focused introduction of thermal energy via sources like laser, electron beam or plasma arcs.

In material extrusion, geometry is deﬁned by matrix material feed via dispensing through a

nozzle or oriﬁce. Thus when being processed, the matrix has to be in ﬂuidic form—a fully or partially

molten polymer or metal, a slurry or suspension.

Material jetting resembles material extrusion, except for the fact that processes are discontinuous

at the micro scale, as materials are provided as droplets, which implies higher ﬂuidity/lower viscosity

material formulations.

Powder bed fusion describes processes in which the building material is spread out as powder

layer or bed, and in which energy is introduced locally to merge the particles e.g., by melting or

sintering processes. Product geometry is deﬁned by this fusion process, while material feed is

geometrically undeﬁned. This allows differentiation from directed energy deposition, in which

materials are selectively deposited, thus creating the part geometry.

Sheet lamination is based on cutting the geometry required in a speciﬁc layer from a foil which is

bonded to the preceding layer. Methods of bonding depend on the materials processed and include

adhesive bonding applied to paper, polymer or metal foils as well as diffusion brazing of aluminum

AlSi12 alloy-coated steel sheets for entirely metallic components (plate diffusion brazing/PDB, plate

press brazing/PPB) [58].

Vat photo-polymerization has probably been, in the form of stereolithography, the one additive

manufacturing process that ﬁrst raised awareness of this class of processes among a wider public.

Common to this and all neighboring processes is the use of photopolymers in liquid state which are

selectively cured either via a mask—a less common variant, as it is less ﬂexible-, or via localized energy

input by means of UV laser scanning of the respective cross-sectional geometry [65].

In a landmark paper on AM economics, Conner et al. discuss the business case for AM

techniques in different scenarios characterized by production volume, product complexity and degree

of customization. The eight basic “regions” this model foresees are depicted in Figure 5 [66].

Sensors 2015, 15, page–page

Figure 5. The defining regions of product characteristics in production volume-complexity-customization

space and the respective potential of AM techniques according to Conner et al. [66].

In their work, Conner et al. start from the notion that across industries, there are controversial

opinions about the value and market potential of AM techniques. They take this controversy as a

motivation for distinguishing between production scenarios (as exemplified in Figure 4) and discuss

the relevance of AM based on this delimitation [66].

Region I is the classic realm of primarily cost-driven mass manufacturing. Investment in

specialized manufacturing equipment can be extremely high in this scenario, as amortization is

spread over very large series. Examples include e.g., sheet metal forming for automotive body

structures. Directly, additive manufacturing typically has no footing here. Indirectly, it can

contribute, though, not by providing the final components themselves, but the tools to make them:

Of these, few are needed, and even using conventional processes like milling in the case of metal

forming or casting dies, their production is a time-consuming affair. In contrast, rapid tooling via

AM processes can offer superior speed and lead time reductions making it an example for Region II,

“Manufacturing of the Few”. The classic idea of Rapid Prototyping falls into this category, too.

Region III comprises products that profit from a level of complexity that cannot be achieved via

conventional manufacturing processes at all, or at least not economically. Once again, examples may

be found in the area of manufacturing tools, where conformal cooling may help to improve product

quality and/or reduce cycle times in processes like plastic injection molding. The basic idea behind

this application is to provide tools with cooling channels that ideally reflect the part geometry,

e.g., by maintaining a constant distance to the inner mold surface. Subtractive manufacturing would

require a modular built-up to come anywhere near to this, whereas AM allows geometry

optimization based on the main functional aspects alone, i.e., thermal, fluid flow and mechanical

considerations. The main driver/enabler here is that, as Connor et al. state, “complexity comes at

no cost” in AM [66]. Similar to the previous, but at higher production volumes, Region IV is

characterized by low levels of customization, but again a high degree of complexity. AM processes

can be competitive in this region if complexity provides enough added benefit to compensate lower

cycle times achieved in conventional processes—or if the latter only reach a mandatory level of

complexity in a sequence of processes that together is more cost- and/or time-intensive than the

single AM process capable of replacing it. Region V adds customization as further parameter, with

production volume and complexity assumed to be low. Personalized consumer products like

mobile/cell phone casings with user-defined features fall into this category. On a more sophisticated

level, customized implants, prostheses or orthoses might be envisaged, as long as complexity

remains low. By stepping up in production volume, but maintaining limited complexity, Region VI

is reached. The catchword here is “Mass Customization”, i.e., the production of large amounts of

individualized products. Thus a business case in this region requires a large market which profits

from individualized solutions that would, realized via conventional processes. More complexity, but

less production volume is what Region VII summarizes. Here, AM can become an enabling

technology that provides the economic means to realize solutions that would otherwise be

unaffordable. Again, medical technology comes to mind, and the possibility to adapt even complex

Figure 5.

The deﬁning regions of product characteristics in production

volume-complexity-customization space and the respective potential of AM techniques according to

Conner et al. [66].

In their work, Conner et al. start from the notion that across industries, there are controversial

opinions about the value and market potential of AM techniques. They take this controversy as a

motivation for distinguishing between production scenarios (as exempliﬁed in Figure 4) and discuss

the relevance of AM based on this delimitation [66].

32087

Sensors 2015, 15, 32079–32122

Region I is the classic realm of primarily cost-driven mass manufacturing. Investment in

specialized manufacturing equipment can be extremely high in this scenario, as amortization is spread

over very large series. Examples include e.g., sheet metal forming for automotive body structures.

Directly, additive manufacturing typically has no footing here. Indirectly, it can contribute, though, not

by providing the ﬁnal components themselves, but the tools to make them: Of these, few are needed,

and even using conventional processes like milling in the case of metal forming or casting dies, their

production is a time-consuming affair. In contrast, rapid tooling via AM processes can offer superior

speed and lead time reductions making it an example for Region II, “Manufacturing of the Few”. The

classic idea of Rapid Prototyping falls into this category, too. Region III comprises products that proﬁt

from a level of complexity that cannot be achieved via conventional manufacturing processes at all,

or at least not economically. Once again, examples may be found in the area of manufacturing tools,

where conformal cooling may help to improve product quality and/or reduce cycle times in processes

like plastic injection molding. The basic idea behind this application is to provide tools with cooling

channels that ideally reﬂect the part geometry, e.g., by maintaining a constant distance to the inner mold

surface. Subtractive manufacturing would require a modular built-up to come anywhere near to this,

whereas AM allows geometry optimization based on the main functional aspects alone, i.e., thermal,

ﬂuid ﬂow and mechanical considerations. The main driver/enabler here is that, as Connor et al. state,

“complexity comes at no cost” in AM [

]. Similar to the previous, but at higher production volumes,

Region IV is characterized by low levels of customization, but again a high degree of complexity. AM

processes can be competitive in this region if complexity provides enough added beneﬁt to compensate

lower cycle times achieved in conventional processes—or if the latter only reach a mandatory level of

complexity in a sequence of processes that together is more cost- and/or time-intensive than the single

AM process capable of replacing it. Region V adds customization as further parameter, with production

volume and complexity assumed to be low. Personalized consumer products like mobile/cell phone

casings with user-deﬁned features fall into this category. On a more sophisticated level, customized

implants, prostheses or orthoses might be envisaged, as long as complexity remains low. By stepping

up in production volume, but maintaining limited complexity, Region VI is reached. The catchword

here is “Mass Customization”, i.e., the production of large amounts of individualized products. Thus a

business case in this region requires a large market which proﬁts from individualized solutions that

would, realized via conventional processes. More complexity, but less production volume is what

Region VII summarizes. Here, AM can become an enabling technology that provides the economic

means to realize solutions that would otherwise be unaffordable. Again, medical technology comes to

mind, and the possibility to adapt even complex prosthesis to the individual bearer. Finally, Region

VIII describes, as Conner et al. put it, the “total manufacturing freedom”: High volume production is

economic, and both complexity and customization carry no added cost. As Connor et al. state, this

region has not yet been entered on a commercial basis—by way of an explanation, they suggest the

still limited production rates and build envelopes of current AM systems. Besides, however, they point

at missing links in the product development chain that impede the efﬁcient take-up of e.g., personal

medical data into prosthesis design [66].

Other motivations and business cases for AM in direct competition with conventional machining

processes include the potential for reduced lead times [

] and time to market (TTM) in general,

the latter with speciﬁc relevance for highly complex parts, where complexity is not only deﬁned

by geometrical or topological considerations, but also by integrated

functionality—Macdonald et al.

speciﬁcally stress the potential of processes that allow integration of electronic components in this

respect [

]. Options of this kind will be treated in more detail below. Besides, repair has been

identiﬁed as promising area of application for speciﬁc AM techniques, though the precondition

usually is that processes rely on feed of material, which excludes approaches like powder-bed fusion,

laminated object manufacture or vat photo polymerization in their classic form. From a more distant

vantage point, replacement of faulty or damaged components is another option in maintenance and

repair which moves the origin of spare parts into focus. Additive manufacturing promises nothing

32088

Sensors 2015, 15, 32079–32122

less than eliminating spare part logistics by producing any necessary component just where it is

needed. Such a decentralized production scenario has been scrutinized by Khajavi et al. based on

F-18 Super Hornet military aircraft component logistics, with the result that economically, current AM

system performance levels and investment costs still favor centralized production. The foreseeable

development of AM machines, including special designs adapted to the spare part task, however,

are expected to reverse this result in a near future [

], with the maritime industry a potential new

customer envisaging improved on-board repair capabilities [69].

Figure 6 provides two examples of AM products and application studies that reﬂect some of the

major areas of interest as deﬁned in the previous paragraph and ﬁgure.

Sensors 2015, 15, page–page

prosthesis to the individual bearer. Finally, Region VIII describes, as Conner et al. put it, the “total

manufacturing freedom”: High volume production is economic, and both complexity and

customization carry no added cost. As Connor et al. state, this region has not yet been entered on a

commercial basis—by way of an explanation, they suggest the still limited production rates and

build envelopes of current AM systems. Besides, however, they point at missing links in the product

development chain that impede the efficient take-up of e.g., personal medical data into prosthesis

design [66].

Other motivations and business cases for AM in direct competition with conventional

machining processes include the potential for reduced lead times [66] and time to market (TTM) in

general, the latter with specific relevance for highly complex parts, where complexity is not only

defined by geometrical or topological considerations, but also by integrated

functionality—Macdonald et al. specifically stress the potential of processes that allow integration of

electronic components in this respect [67]. Options of this kind will be treated in more detail below.

Besides, repair has been identified as promising area of application for specific AM techniques,

though the precondition usually is that processes rely on feed of material, which excludes

approaches like powder-bed fusion, laminated object manufacture or vat photo polymerization in

their classic form. From a more distant vantage point, replacement of faulty or damaged components

is another option in maintenance and repair which moves the origin of spare parts into focus.

Additive manufacturing promises nothing less than eliminating spare part logistics by producing

any necessary component just where it is needed. Such a decentralized production scenario has been

scrutinized by Khajavi et al. based on F-18 Super Hornet military aircraft component logistics, with

the result that economically, current AM system performance levels and investment costs still favor

centralized production. The foreseeable development of AM machines, including special designs

adapted to the spare part task, however, are expected to reverse this result in a near future [68], with

the maritime industry a potential new customer envisaging improved on-board repair capabilities

[69].

Figure 6 provides two examples of AM products and application studies that reflect some of the

major areas of interest as defined in the previous paragraph and figure.

Figure 6. Examples of products and case studies representing major regions of interest of AM

technology application: (a) A plastic injection molding die featuring conformal cooling channels [70],

an example of the Rapid Tooling approach representative of region II and III according to

Connor et al. [66] and as depicted in Figure 5; (b) low weight, low flow resistance hydraulic crossing

[70], region III and IV.

2.3.2. Approaches towards Sensor Integration in AM

Sensor and electronics integration to realize material-integrated intelligent systems, sensorial or

robotic materials or structural electronics components is currently the object of many research

activities fueled by application scenarios like structural health monitoring (SHM), smart skins for

robotics and prosthetics and smart components forming part of the Internet of Things (IoT) [71–75].

In such contexts, interest in AM-based sensor integration is likewise on the rise. As a first step, such

Figure 6.

Examples of products and case studies representing major regions of interest of AM

technology application: (

) A plastic injection molding die featuring conformal cooling channels [

], an

example of the Rapid Tooling approach representative of region II and III according to

Connor et al. [66]

and as depicted in Figure 5; (

) low weight, low ﬂow resistance hydraulic crossing [

], region III

and IV.

2.3.2. Approaches towards Sensor Integration in AM

Sensor and electronics integration to realize material-integrated intelligent systems, sensorial

or robotic materials or structural electronics components is currently the object of many research

activities fueled by application scenarios like structural health monitoring (SHM), smart skins for

robotics and prosthetics and smart components forming part of the Internet of Things (IoT) [

–

In such contexts, interest in AM-based sensor integration is likewise on the rise. As a ﬁrst step,

such attempts require multi-material processing in AM, and in fact, such solutions are available. A

fundamental example is processing of homogeneous composite or hybrid materials without gradients

in composition, and without controlled orientations e.g., of reinforcing particles. Here, the main

challenge is to rule out de-mixing of materials during the process. This is speciﬁcally critical when

fractions of particulate materials deviate considerably e.g., in size or density. Tools for analyzing such

problems include analysis of particle ﬂow through discrete element method (DEM) modeling [

Despite this issue, several of the basic AM techniques can be modiﬁed for multi-material processing,

e.g., by provision of suitable, stable powder mixtures, reinforcement phase particles coated with the

foreseen matrix materials, and direct processing of particulate composite materials in powder bed

fusion processes, by use of dispersions of a reinforcing material in a photopolymerizable material in

vat photopolymerization or by adding the second phase as component of the binder in binder jetting

processes. Kumar and Kruth, and more recently Vaezi et al., have provided reviews covering the

respective materials and process variants [77,78].

The challenge is much harder to tackle if a spatially deﬁned change in composition or properties

is envisaged, as this implies the need for locally controlled variation of materials and/or material

properties. Guo and Leu thus distinguish between the aforementioned uniform composites on the

one hand and functionally graded materials (FGM) on the other [

]: Compared to a layer-to-layer

32089

Sensors 2015, 15, 32079–32122

gradient along the building direction as most fundamental realization of an FGM through AM a new

level of complexity is reached if variation needs to be clearly deﬁned in three dimensions. This is the

case when sensors, electric and electronic interconnects and ﬁnally electronic components themselves

are to be generated during the building process, using the same manufacturing system. The promise

of such components, and the potential of AM to deliver them, has long since been recognized [

]: It is

a fundamental aspect of our current scenario.

Suitability of AM process classes for the task varies depending on the principle of shape generation:

Processes which establish object geometry by locally consolidating a geometrically simple volume of

material are less suited than those in the course of which the material is deposited locally. The latter

facilitate a change of material during the building process, e.g., by switching between reservoirs in

dispensing or extrusion processes. Material jetting and material extrusion, but also directed energy

deposition processes fall into this category—powder bed fusion and vat photopolymerization represent

the opposite class. Binder jetting, in contrast, assumes an intermediate position, as this process

combines local deposition of material with a material reservoir, notably a powder bed. Here, the

possibility of applying the binder to locally modify the powder bed material has been demonstrated

with respect to spatial control of carbon content levels, and thus of hardenability, in steel parts [81].

Present attempts at sensor integration typically follow either of two different paths: The ﬁrst

is best described as combination of production processes, where separate processes like functional

printing ([

]) account for adding secondary and ternary materials serving functional purposes

like sensing or electric insulation (see Table 2, manufacturing principle “separate process”, subclasses

“part/part section as substrate” and “separate process, hybrid manufacturing system”). The second

option is ex-situ production of sensing elements and integration of these as subcomponents in the

course of the build-up of the ﬁnal part (“separate process & substrate” in Table 2). Essentially, it

combines assembly via pick-and-place operations with the classic, additive building processes. The

former strategy is attractive because of the neat integration of sensors into the structure. Depending

on the underlying AM process, it can either be realized by using multiple manufacturing systems

intermittently (ex situ), or by implementing secondary processes suitable for sensor integration in an

AM manufacturing system—typically this could be a printing process (in situ). The second approach,

on the other hand, allows for integration of intelligent sensor systems rather than individual sensors

and is feasible already today.

The obvious third option, and a long-term objective at that, would need adaption of the AM

process itself towards an in-process switch of materials, including the option to create geometrically

deﬁned structures from all materials involved (“same process” in Table 2). For component-integrated

sensors, this approach will require handling several materials in parallel, since conductive paths

and insulations thereof as well as the different integrated components’ functional requirements

would demand highly specialized individual materials. Due to this complexity, for the time being

multiple processes in a single setup seems a viable intermediate step.Besides, since achievable levels of

complexity even in upgraded or hybrid AM systems will fall short of dedicated IC production process

capabilities for any foreseeable future, combination of direct building and assembly techniques will

remain the preferred short- and mid-term path towards highly capable sensorized AM parts.

32090

Sensors 2015, 15, 32079–32122

Table 2. Exemplary case studies on AM products with integrated or applied sensors, sensors systems, PEIDs etc.

Integration Level

Manufacturing

Principle

Description (Principle)

Material (Product)

AM Process

Reference

Polymer Metal Ceramic

1—surface

application &

integration

1.1—separate process &

substrate

1.1.1—Application of SMT components

on the surface of AM parts.

X SLA [84]

1.2—separate process,

part as substrate

1.2.1—Aerosol Jet printing of

conductive paths for powering LEDs

and propellers of a small scale UAV

produced via FDM process.

X FDM [85]

1.2.2—Provision of interconnects for

surface-mounted devices via Aerosoal

Jet printing on curved surfaces, linked

to example 1.1.1.

X SLA [84]

2—2D volume

integration

2.1—separate process &

substrate

2.1.1—Adaptation of 3DP process for

creation of cavities to hold electronic

components, integration and electrical

connection of same using inkjet printing

(X) (X) 3DP [84]

2.2—separate process,

part (section) as substrate

2.2.1—Ultrasonic Cu wire bonding on

FDM substrate and subsequent over

molding as encapsulation step for

production of interconnects between

electronic components and of sensors.

X FDM [86]

2.2.2—Screen printing of insulating

layers on Al foil, joining of foils using a

sonotrode in an ultrasonic additive

manufacturing (UAM) variant of the

LOM process

X UAM/UC [87,88]

32091

Sensors 2015, 15, 32079–32122

Table 2. Cont.

Integration Level

Manufacturing

Principle

Description (Principle)

Material (Product)

AM Process

Reference

Polymer Metal Ceramic

3—3D volume

integration

3.1—separate process &

substrate

3.1.1—Manual placement of separately

manufactured electronic components

(LEDs) and/or sensors within cavities

of a SLA part.

X SLA [67]

3.2—separate process,

part as substrate

3.2.1—3D and cross-plane via holes for

electrical interconnects produced in a

3DP ceramic part, conductivity

achieved via electro-less copper plating

adapted from PCB production, surface

mounted electronic components

according to a 3.1 principle.

X 3DP [89]

3.3—separate process,

hybrid manufacturing

system

3.3.1—SLA system upgraded with an

ink dispenser system for direct writing

of laser-cured silver ink based

conductive paths, suitable for 2D and

3D paths.

X SLA [88]

3.3.2—FDM combined with inkjet

printing system in a single,

commercially available device offered

by startup company Voxel8. Full

3D-integration of conductive paths etc.

plus external, manual pick-and-place

capability.

X FDM [90,91]

3.3.3—Hybrid manufacturing system

comprising FDM, ink dispensing,

thermal embedding of wires,

micromachining, robotic pick & place

assembly in one system to allow “single

setup” realization of smart objects.

X FDM [92]

3.3.4—Placement of separately

manufactured SMT components within

cavities of a 3D Printing part created via

integrated suction-based powder

withdrawal system, also used for

automated pick-and-place operation.

(X) (X) 3DP [84]

32092

Sensors 2015, 15, 32079–32122

Figure 7 summarizes the possible variants of sensor and sensor or smart system integration

in an AM part based on a similar depiction by Hoerber et al. [

]. Table 2 grants an overview of

studies detailing several of these and sorts them according to the achieved level of integration and the

manufacturing principle applied. The former extends from external (surface) application via surface

integration to volume integration. As discussed, volume integration can once more take two forms, the

ﬁrst comprising approaches in which the individually built-up production layers act as deﬁning planes

for an essentially two-dimensional integration, the second a true three-dimensional integration which

transgresses these limits. In the following, we will speak of 2D vs. 3D volume integration wherever

it is necessary to highlight the difference. Beyond integration level, the manufacturing principle as

developed in the preceding paragraph helps structuring the table.

Sensors 2015, 15, page–page

Figure 7 summarizes the possible variants of sensor and sensor or smart system integration in

an AM part based on a similar depiction by Hoerber et al. [84]. Table 2 grants an overview of studies

detailing several of these and sorts them according to the achieved level of integration and the

manufacturing principle applied. The former extends from external (surface) application via surface

integration to volume integration. As discussed, volume integration can once more take two forms,

the first comprising approaches in which the individually built-up production layers act as defining

planes for an essentially two-dimensional integration, the second a true three-dimensional

integration which transgresses these limits. In the following, we will speak of 2D vs. 3D volume

integration wherever it is necessary to highlight the difference. Beyond integration level, the

manufacturing principle as developed in the preceding paragraph helps structuring the table.

Figure 7. Overview of possibilities for combining sensors and sensor or smart systems with AM

parts, inspired by a similar schematic image by Hoerber et al. [84]. The numbering corresponds to

categories in Table 2 as follows: No. 1 matches category 1.2, 2 equals 2.2, 3 = 3.1 & 3.3, 4 = 1.1, 5 = 1.2, 6

= 3.1 & 3.3, 7 = 3.3.

Macdonald et al. have recently provided a study, also cited in Table 2 above, in which 3D

volume integration of conductive paths is combined with individual components the addition of

which constitutes the full circuit. The product is a gaming die capable of detecting its position and

indicating it via integrated LEDs that represent the typical face patterns associated with the various

numbers. The basic process used is stereolithography, during which buried tracks for conductive

paths and openings for electronic components are foreseen in the AM part, with component

placement realized through state of the art pick-and-place operations and filling of the tracks with

printable inks, both necessarily done separated from the preceding AM step. As the process chain

relies on 2D design of the electronic system, and folding of the respective planar layout to achieve

positioning on the six faces of the die, in Table 2, the study must be considered an example of 2D

surface integration in a separate process and using the final part as substrate. As a specific challenge,

Macdonald et al. mention the lack of software tools for true 3D layout of circuitry, as the available

systems typically address planar PCB design and allow at best folding of the finished 2D design, or

wrapping it around geometrically simple objects. In effect, the full potential of 3D integrated

electronics cannot be realized, and for the present system, MacDonald et al. had to rely on a

combination of conventional circuitry layout and 3D CAD systems [67].

In a follow-up study by the same working group, Espalin et al. describe the features of an

integrated manufacturing system combining several production and assembly processes to

overcome some of the shortcomings of the previous device. Among these are the strength of SLA

matrix materials and the conductivity of interconnects realized via dispensing of inks, which were

both felt to be too low. The solutions adopted by Espalin et al. include switching from SLA to FDM

and thermoplastics as well as adding a system for copper wire thermal embedding [92]. About the

latter, Shemelya et al. report in more detail [86]. The ink dispenser system, however, was retained for

realizing those interconnects either not requiring highest conductivity levels, or transgressing the

building planes. Since FDM resolution does not attain the respective SLA capabilities, for building

channels to hold ink-based conductive paths at sufficiently low pitch values, a micromachining

system was added. Besides, an automated pick-and-place system is available for integration of

Figure 7.

Overview of possibilities for combining sensors and sensor or smart systems with AM parts,

inspired by a similar schematic image by Hoerber et al. [

]. The numbering corresponds to categories

in Table 2 as follows: No. 1 matches category 1.2, 2 equals 2.2, 3 = 3.1 & 3.3, 4 = 1.1, 5 = 1.2, 6 = 3.1 & 3.3,

7 = 3.3.

Macdonald et al. have recently provided a study, also cited in Table 2 above, in which 3D volume

integration of conductive paths is combined with individual components the addition of which

constitutes the full circuit. The product is a gaming die capable of detecting its position and indicating

it via integrated LEDs that represent the typical face patterns associated with the various numbers. The

basic process used is stereolithography, during which buried tracks for conductive paths and openings

for electronic components are foreseen in the AM part, with component placement realized through

state of the art pick-and-place operations and ﬁlling of the tracks with printable inks, both necessarily

done separated from the preceding AM step. As the process chain relies on 2D design of the electronic

system, and folding of the respective planar layout to achieve positioning on the six faces of the die,

in Table 2, the study must be considered an example of 2D surface integration in a separate process

and using the ﬁnal part as substrate. As a speciﬁc challenge, Macdonald et al. mention the lack of

software tools for true 3D layout of circuitry, as the available systems typically address planar PCB

design and allow at best folding of the ﬁnished 2D design, or wrapping it around geometrically simple

objects. In effect, the full potential of 3D integrated electronics cannot be realized, and for the present

system, MacDonald et al. had to rely on a combination of conventional circuitry layout and 3D CAD

systems [67].

In a follow-up study by the same working group, Espalin et al. describe the features of an

integrated manufacturing system combining several production and assembly processes to overcome

some of the shortcomings of the previous device. Among these are the strength of SLA matrix materials

and the conductivity of interconnects realized via dispensing of inks, which were both felt to be too low.

The solutions adopted by Espalin et al. include switching from SLA to FDM and thermoplastics as well

as adding a system for copper wire thermal embedding [

]. About the latter, Shemelya et al. report in

more detail [

]. The ink dispenser system, however, was retained for realizing those interconnects

either not requiring highest conductivity levels, or transgressing the building planes. Since FDM

resolution does not attain the respective SLA capabilities, for building channels to hold ink-based

conductive paths at sufﬁciently low pitch values, a micromachining system was added. Besides, an

32093

Sensors 2015, 15, 32079–32122

automated pick-and-place system is available for integration of electronic components in the ﬁnal

part. Not yet a part of the full, multi-technology manufacturing system, but externally tested and

suggested for integration is a laser micro welding system capable of joining electronic components and

conductive paths. Similarly, laser ablation is considered as alternative to micromachining for realization

of cavities and high-resolution channels for conductive paths. Currently the resulting multi

system

is probably the most sophisticated solution for realizing electronics-embedded polymer components.

Its fundamental philosophy is integrative, and not generic, though [

]. A somewhat simpler, but

basically similar system is offered on a commercial basis by the Arvard University spin-off Voxel8,

which offers a printing system that comprises a ﬁlament-based FDM process in combination with

an integrated inkjet printhead. On top of this dual-process setup, building platform positioning

and ﬁxing has been adapted to allow interruption of the building process, removal of the building

platform for external processing (e.g., pick-and-place operations for addition of electronic components),

replacement of the building platform and continuation of component build-up right from the point

where it was interrupted [90].

Summing up the current state of research on embedding sensor systems in additively

manufactured structures, to the authors’ best knowledge, the following deﬁciencies prevail:

There is no single process that allows, through variation of materials and/or process parameters,

the direct realization of 3D volume integrated sensor systems, or has been evaluated towards this end.

Polymer-based solutions dominate the ﬁeld, since both metals and ceramics typically mean

harsher manufacturing conditions, and metals increase complexity further by requiring insulation

layers separating functional units from the (conductive) bulk.

Technologies like printing of conductive paths often fail to reach theoretically possible

performance levels because of limitations imposed by other system components, e.g., thermal stability

of polymers capping the applicable sintering temperatures.

Of all available options, directed energy deposition, with the LENS

[

], DLF [

] and DMD [

]

processes as examples, is probably the most likely candidate for overcoming all these obstacles in a

single process, as the principle allows switching of materials easily and is suitable for handling metals

and ceramics. If sensor integrated AM parts in general are sought for, however, a suitable combination

of processes may seem a more favorable solution. Conclusion is, however, that in commercialization of

such integrative solutions, it is not so much new technological breakthroughs that are required, but a

further development and optimization of existing concepts to support economic viability. Voxel8 has

done a very important ﬁrst step in this direction [

]. Thus for the realization of the present paper’s

vision, the ground is already prepared.

2.4. Usage Data Supported (Automated) Design

In this subsection, various state-of-the-art design approaches using usage or PLM data, collected

by sensors or otherwise, are elaborated. Furthermore, a snapshot of different application cases and

current research projects related to this topic is presented. Automation of design tasks is subject of

research for at least two decades. In this paper, two main approaches for design automation are

described, i.e., Knowledge-Based Engineering (KBE) and the design of adaptive products.

As design is a knowledge intensive task, the research stream of KBE investigates approaches to

capture and re-use product and process engineering knowledge. KBE seeks to automate repetitive

and non-creative tasks, however, leaving more difﬁcult decisions to (human) designers. The principles

behind the automation of repetitive design tasks is often facilitated by the use of Ontologies, semantic

connection of knowledge and a framework of independent formalization of knowledge [

]. Criticism

towards current KBE approaches include their mostly case-based nature, black-box application, lack

of knowledge re-use, lack of quantitative assessment of cost and beneﬁts and thus the lack of a

quantitative framework to judge the value-add of KBE development [

]. On the other hand there are

several successful examples of KBE implementation available. If successful, this offers not only more

time for human designers to focus on advanced design tasks.

32094

Sensors 2015, 15, 32079–32122

Another approach of design automation concerns the integration of evolutionary concepts,

lent from biology, into engineering design. Beal suggests to apply self-organization approaches

in engineering to enable adaptive design [

]. Products having the ability to adapt, are more

robust in situations where initially deﬁned system requirements change. According to Beal, a key

concern, however, is that initial product design becomes more difﬁcult, as adaptation problems

must be faced that would be otherwise postponed or overlooked. A similar attempt to combine

engineering and evolutionary concepts is summarized by Denkena et al. [

]. Their research work was

conducted over several years within the Collaborative Research Center 653 “Gentelligent Components

in Their Lifecycle” (Germany). The approach aims for the collection and storage of product

information from within the product with minimum additional electronic components. The scenarios

include modiﬁcation of materials to make them sensitive, in a very broad sense, e.g., to mechanical

loads experienced in use. An example is the mechanically induced martensitic transformation of

non-magnetic austenite phases in steel. The ensuing change in magnetic characteristics can be detected

upon inspection by means of an external sensor system, or continuously via surface-attached or

surface-integrated sensors. Its level is interpreted as an indication of peak loads experienced by the

component. In an associated case study, these data are used for mechanical structure optimization of a

wheel suspension by employing a genetic algorithm and a parametric design model [

]. Parametric

design models allow the rapid adaption of design features by incorporating design rules into a design

model. These rules take into account that the change of one parameter of a product (e.g., a length of a

handle) may impact other features of the product. Hehenberger states that products have different

degrees of standardization (or likewise parametrization) depending on cost and quality aspects [96].

In terms of design automation, there are also associated approaches that do not seek to automate

the creation of actual designs, for instance in the form of CAD models, but instead focus on making

the identiﬁcation of product failures easier. Product failures affect user satisfaction and thus must

be addressed by producers. The established “rule of ten” states that failures, identiﬁed early in the

process are by magnitudes less costly than failures identiﬁed during the usage phase [

]. If failures

can be traced back to design shortcomings, the design can be adapted. Example cases that apply usage

information, in order to detect and address product failures are provided in [

–

100

]. A selection of

use cases applying usage information in the context of product design are summarized in Table 3.

Table 3. Examples of products collecting usage information to support design decisions.

No. Product/Area Ref. Purpose/Goal

Considered Usage

Information (Examples)

1 Hydraulic system [101]

Identiﬁcation of weak

points of parent

generation and

support in design of

next generation

Temperature (inside

system and environment),

pressure, rotation speed,

vibration, time of use,

load during use, time of

failure, kind of failure

2 Rotary spindle unit [100]

Fault diagnosis of

drive belt using

Bayesian network

Spindle rotation speed,

spindle running time,

ambient temperature, last

maintenance

3 HVAC [102]

Identify potential

product failures

Not explicitly mentioned.

4 Robot [14]

Design adaptation

using

self-organization

Not explicitly mentioned.

5 Furniture [103]

Quantiﬁcation of

furniture

deterioration using a

sensor network

Human use of the

product and product

wear, pollution, light,

relative humidity

32095

Sensors 2015, 15, 32079–32122

Table 3. Cont.

No. Product/Area Ref. Purpose/Goal

Considered Usage

Information (Examples)

Vehicle fuel injection

system

[104]

Method for use of

warranty data to

improve products

Warranty data (not

further speciﬁed)

7 Leisure boats [20]

Improvement of boats

in general

Atmospheric pressure,

external temperature, air

humidity and engine

RPM, geo position

Spur gear

manufacturing

[105]

Improvement of

manufacturing

process

Geometry information of

the currently produced

gear

Injection molding

machine

[106]

Support for ﬁnding

temperature

inconsistencies

during molding

process

Temperatures, pressure of

the turning screw,

produced product quality

(binary)

Machine tool

(spindle)

[107]

Remaining useful

component lifetime

estimation

Vibration, motor power,

rounds per minute and

speed, temperatures,

number of start/stop and

acceleration/retardation,

travel length, number of

alarms, running time

Wheel carrier

component

[95]

Identiﬁcation of

develop-relevant load

cases and adaptation

of design using a

genetic algorithm

Forces

12 Boats [108]

Estimate potential for

improvement of boats

Service information, boat

data, customer-provided

data (not further

speciﬁed)

13 Locomotive [11]

Correlation of design

parameters with ﬁeld

data that inﬂuence

working status of

product

Brake cylinder pressure,

locomotive acceleration,

heating circuit current,

ﬁlter current, catenary

voltage, mileage

Most of the cases described in literature focus on the identiﬁcation of failures and their root causes.

The actual automated change of design parameters, however, is rarely addressed. The examples

indicate that a variety of actual information is collected and used. Besides information about the

environment (e.g., temperature and humidity), also performance indicators of the product are relevant

(e.g., acceleration and power consumption). In case of production machinery (or products creating a

usable output), also quality parameters of the output are relevant (e.g., geometry).

3. Usage-Data Enhanced (UDE-) CBDM: A New Process Model Rooted in Cloud-Based Services

The underlying concepts of CBM, CMfg and CBDM have been discussed in the introductory

section. The proposed criteria, describing the extensions we recommend, have also been formulated

in that section (Figure 2). Based on these added requirements, the state of the art in the respective

technological domains has been scrutinized in Section 2: apparently, most of the basic ingredients

needed for an extension of CBDM towards UDE-CBDM are already available or at the very least under

development. Despite this encouraging fact, we hope to be able to pinpoint a number of white spots

that still deserve further research efforts, and more that need development towards commercialization:

These will in part be introduced here, illustrated by the case studies, and detailed in the discussion.

32096

Sensors 2015, 15, 32079–32122

Figure 8 outlines the process sequence we proposed in terms of a process direction and data ﬂows

linked to the individual process steps.

We assume that the initial motivation to introduce a new product, as well as its initial design, both

follow conventional approaches: We call this initial design the root design, as it is the common ancestor

and thus the foundation of all variants that may be generated at later stages within the scheme. This

root design may, in a subsequent, optional step, be customized to meet the requirements of a speciﬁc

individual user, or a user group. Descriptions of both designs will then, as the vertical data ﬂow paths

suggest, be added, as initial entries, to a related data repository: In it, the root design description is of

global character, whereas a possible adaptation to a user or user group would bear some reference to

these. The provision of the respective data repository denotes the ﬁrst cloud-based service encountered

in the processing sequence.

Sensors 2015, 15, page–page

requirements of a specific individual user, or a user group. Descriptions of both designs will then, as

the vertical data flow paths suggest, be added, as initial entries, to a related data repository: In it, the

root design description is of global character, whereas a possible adaptation to a user or user group

would bear some reference to these. The provision of the respective data repository denotes the first

cloud-based service encountered in the processing sequence.

Figure 8. Process scheme describing main activities and data flows in usage-data enhanced cloud

based design and manufacturing (UDE-CBDM).

In the case of the initial product variant, the next steps would focus on tasks directly associated

to the manufacturing processes. This includes the identification of suitable manufacturing resources

and the allocation of the manufacturing task to these, based e.g., on a cloud-based quoting system as

suggested in several CBDM definitions. An added feature in a real-world implementation of

UDE-CBDM is that manufacturing would heavily rely on AM processes. These would contribute

their generic flexibility, thus facilitating almost immediate fabrication of product variants

representing new adaptations of the root design. The “almost” in the preceding sentence is owing to

the fact that new geometries will require a revision of the manufacturing, i.e., build-up, strategies

(e.g., scanning patterns in SLS, SLM or SLA processes), in order to minimize adverse effects like

residual stresses and the like: The assumption is that initial information on this issue may have been

provided with the root design, but any geometry or material-related alteration would require further

checking, and almost invariably updating of the respective information for the adapted design. The

general UDE-CBDM concept leaves open whether such tasks are associated with the DaaS toolset, or

alternatively with the manufacturing side. Handling this at the design stage would be the preferred

option, though, to make sure that no design unfit for production is issued. Any design adapted by

the DaaS tool would be added to the UDE-CBDM data repository. Assuming a highly parametric

design, this could be done, for instance, through storage of the relevant design parameter values—or

at least of those that deviate from the root design. The adapted design should then also be linked to

the data set on which it had been based. The reasoning behind this measure is that a new design for

the same user or user group could potentially be derived more easily from an evolved product state

than from the initial, root design.

Collection of usage data would start at the latest following the delivery of the product to the

customer. This data would again find entry into the aforementioned data repository, where it would

Figure 8.

Process scheme describing main activities and data ﬂows in usage-data enhanced cloud

based design and manufacturing (UDE-CBDM).

In the case of the initial product variant, the next steps would focus on tasks directly associated

to the manufacturing processes. This includes the identiﬁcation of suitable manufacturing resources

and the allocation of the manufacturing task to these, based e.g., on a cloud-based quoting system

as suggested in several CBDM deﬁnitions. An added feature in a real-world implementation of

UDE-CBDM is that manufacturing would heavily rely on AM processes. These would contribute

their generic ﬂexibility, thus facilitating almost immediate fabrication of product variants representing

new adaptations of the root design. The “almost” in the preceding sentence is owing to the fact that

new geometries will require a revision of the manufacturing, i.e., build-up, strategies (e.g., scanning

patterns in SLS, SLM or SLA processes), in order to minimize adverse effects like residual stresses and

the like: The assumption is that initial information on this issue may have been provided with the root

design, but any geometry or material-related alteration would require further checking, and almost

invariably updating of the respective information for the adapted design. The general UDE-CBDM

concept leaves open whether such tasks are associated with the DaaS toolset, or alternatively with

the manufacturing side. Handling this at the design stage would be the preferred option, though, to

make sure that no design unﬁt for production is issued. Any design adapted by the DaaS tool would

be added to the UDE-CBDM data repository. Assuming a highly parametric design, this could be

done, for instance, through storage of the relevant design parameter values—or at least of those that

deviate from the root design. The adapted design should then also be linked to the data set on which

it had been based. The reasoning behind this measure is that a new design for the same user or user

32097

Sensors 2015, 15, 32079–32122

group could potentially be derived more easily from an evolved product state than from the initial,

root design.

Collection of usage data would start at the latest following the delivery of the product to the

customer. This data would again ﬁnd entry into the aforementioned data repository, where it would

be associated with the root design and any pre-existing design variant, as well as to the individual

user and/or to the user group he/she belongs to.

Any triggering event that would initiate production of a second variant of the root design

would not be channeled into the conventional design path, but would rather be diverted to the

DaaS tool offered as a cloud-based service. This tool would draw on root design information and

usage information available from the UDE-CBDM data repository. Besides, direct user feedback and

information might be available from the latter. Finally, if connections between product usage data

and other cloud-based sources of information can be made, additional information, e.g., on the usage

context, could be gathered. Interfacing and mapping between the data repository and DaaS tool would

require a dedicated tool, which would constitute a central element of the entire concept.

Depending on the capabilities of the DaaS toolset, the concept could allow the customer to directly

trigger the manufacturing process simply by placing his order, up to a level without any human

intervention in a fully automated design process linked to a likewise automated process handling the

manufacturing organization up to the ﬁnal logistics that would guarantee that the product reaches the

user. From a business model perspective, this notion has several interesting implications—companies

that originally developed the root design might not be selling products then, but licensing designs

instead. Alternative DaaS provides might compete for customers. Access to usage and associated data

might become a trading good and as such be offered by specialized service providers. We will discuss

such issues in a little more detail in Section 5, and speciﬁcally in Sections 5.1 and 5.2.

With more and more products sharing a common root design on the market, the UDE-CBDM

data repository would grow, allowing more and more discrimination between user groups etc.

The separation between the establishment of a root design on the one hand and an initial

customization on the other makes sense because, in order to facilitate the later design adaptation,

deﬁnition of this root design must be ﬂexible in any case. Enhanced parametric design could be a

useful approach in this respect, as it would provide the main design parameters as initial, primary

handles for adaptation. Foreseeing this facility from the start could then also support any initial

customization, if needed. In an implementation of this kind, however, the initial designer must have

in mind that the way in which his root design is parameterized may determine the envelope within

which future design adaptations can occur, as any deviation from this framework might require a

partial re-parameterization of the design, and thus a considerable extension of both width and depth

of the design adaptation task. In practice, this would imply that automated design adaptation might

only reach local, not a global optimum, depending on the level and extent of any pre-determination

contained in the fabric of the root design.

The DaaS toolset itself can be seen as a very speciﬁc and very complex example of (at least) a SaaS

system. Due to the complexity, which will be discussed in Section 5, where we intend to show that not

only the designer perspective, but also the regulatory one is of primary importance with respect to this

service, we have nevertheless opted to coin the DaaS label here.

4. Case Studies

In this section, three case studies transfer the previously presented UDE-CBDM concept to actual

application environments. The aim of this section is not to present a comprehensive and evaluated

“ready-to-use” solution, but to show the potential of the vision and provide a basis for future critical

discussion of the topic. The ﬁrst case study focuses on medical implants, which are by deﬁnition

one-of-a-kind and highly customized based on the users’ preferences, state and physique. At the

same time, they are not extremely complex compared to other multi-component products. Hence,

this environment provides a solid frame for the envisioned application, and a frame at that in which

32098

Sensors 2015, 15, 32079–32122

Additive Manufacturing is already a competitive production process and sensor integration has

been used to derive valuable usage information on a general, not individual level in the course of

several research projects. The second case study describes “the other extreme”, with a scenario set

in the robotics domain. In this case, the natural ability of robots to collect and communicate usage

data through their own, not necessarily monitoring-, but task-oriented sensors and communication

equipment is used in order to enhance this implementation. The focus is very much on data processing

techniques, with agent-based solutions identiﬁed as the method of choice, and the linking of these to

the overall scenario as laid out.in Section 3.The section is closed by a third case study representing a

more detailed study of a scenario based on the design and production of shoes which foresees usage

data backﬂow and personalized design solutions based on these. This example is based on a dedicated

research project conducted in Japan, which as such goes beyond the preceding studies in being much

nearer to implementation. Not covered at present is the envisaged automated redesign capability

provided by the DaaS toolset.

4.1. Case Study 1: Medical Implants—Generation-to-Generation Adaption Based on Strictly Individual

Use Patterns

For several types of medical implants, consideration and even practical application of AM

processes is commonplace, as by deﬁnition, they need to be individual in their adaption to an individual

patient [109–111]. Business cases in this respect have been highlighted in Section 2.

A general aspect of several types of implants is that they are subject to inﬂuences like wear and

degradation and thus may need replacement. Any such replacement means surgery, with all the risks

this entails for the patient. This constitutes a strong motivation for increasing the lifespan of an implant.

Hip implants, to consider a concrete example, are both common (at 300,000 replacements performed in

the United States in 2010 alone and 80,000 annually in the UK, according to Kandala et al. [

112

]) and

heavily loaded. At the same time, they are subject to revision surgery in a relevant number of cases.

Studies from the UK have considered revision rates after a given time as a benchmark for different

products and indicate that an aim should be set to achieve, for a given product, a likelihood of revision

of less than 5% within a 10 year period [112].

If production of the implant in question is AM based anyway, manufacturing of an altered part

is a realistic option, if there is a sound justiﬁcation of a redesign, and if economics of the redesign

process can be covered: manufacturing costs, in contrast, are unlikely to change signiﬁcantly compared

to the initial part. However, the primary concern when it comes to redesigning an implant for the

individual patient is the availability of personal data. The High Road to gathering such data is

equipment of the implant with a suitable set of sensors allowing read-out of raw data or processed

information at any time. Smart implants of this kind have been realized for several body areas,

including the aforementioned case of a hip replacement, but also the shoulder, spine or knee, as bone

or cartilage substitutions—an overview in this respect has recently been published by

Ledet et al. [113]

Sensor-integrated systems of this kind have provided extremely valuable information for guiding

implant design in general terms by measuring forces, moments or temperatures associated with

different designs and different use patterns. They have furthermore contributed to an improved

understanding of the musculoskeletal system, and of healing processes following the implantation—all

this on a level that would hardly have been accessible through other means. However, in performing

these tasks, up to the present day, they have remained tools for research rather than becoming a

standard aid to the implant designer, far less the average patient.

Going back to the example of the hip implant, typical early smart implant designs incorporated

three-axis force measurements [

114

], later ones recorded bending moments and temperature, too [

115

In the latter variant, sensor system and signal processing are integrated in the hip implant’s neck,

while the antenna used for telemetric data transmission to an external evaluation unit is located inside

its ceramic head. The implanted system receives the necessary power via inductive coupling using a

32099

Sensors 2015, 15, 32079–32122

coil linked to the external evaluation and power supply unit, and a matching one again integrated

with the implant [

115

–

117

]. System sizes have dropped from cm to the mm length scale reached today.

Besides smartness, adaptation of implants to the speciﬁc requirements of individual patients is

a constantly studied issue, but the point of attack is typically not information on the patient-speciﬁc

usage: In a recent study, Campoli et al. report about reconstruction of the link between internal bone

structure and musculoskeletal forces using CT data, FEM modelling and optimization techniques for a

scapula [

118

119

]. Similar principles could be used to determine likely implant loads and in this role

might at least partly replace direct measurement of forces through smart implants, or compensate their

non-availability when ﬁrst implanting the respective bone or joint replacement. However, approaches

of this kind, though building on observable individual structures and geometries, have to rely on

load cases that are mere conjectures. Besides, they are extremely complex and incorporate several

assumptions e.g., in terms of

in vivo

mechanical characteristics of bones and tissue, which makes

automation a major area of study in this ﬁeld [

120

]. In effect, this means that nowadays patient-oriented

implant adaptation primarily addresses the geometry of interacting surfaces, as in the case of the hip

joint, or the geometry of the bone in which the implant would be embedded, i.e., the femur in the case

of a hip replacement. Consideration of local density variation of the femur may similarly be taken into

account based on CT measurements and associated local density derivation [121].

Additive manufacturing of various types of implants has gained in importance in parallel

to the rise to maturity and fame of the technology and as a means towards realizing the

aforementioned patient-speciﬁc adaptations. A recent study by Cronskär et al. has demonstrated

a cost advantage of 35% when manufacturing a series of 7 hip stem implants using EBM-AM

rather than conventional (subtractive) manufacturing techniques [

121

]. The basis for implant layout,

irrespective of the production path chosen, is a CT scan and a derived 3D representation of the

receiving bone (femur). This established design approach is illustrated in Figure 9 and contrasted to its

UDE-CBDM counterpart.

Sensors 2015, 15, page–page

a scapula [118,119]. Similar principles could be used to determine likely implant loads and in this

role might at least partly replace direct measurement of forces through smart implants, or

compensate their non-availability when first implanting the respective bone or joint replacement.

However, approaches of this kind, though building on observable individual structures and

geometries, have to rely on load cases that are mere conjectures. Besides, they are extremely complex

and incorporate several assumptions e.g., in terms of in vivo mechanical characteristics of bones and

tissue, which makes automation a major area of study in this field [120]. In effect, this means that

nowadays

patient-oriented implant adaptation primarily addresses the geometry of interacting surfaces, as in

the case of the hip joint, or the geometry of the bone in which the implant would be embedded, i.e.,

the femur in the case of a hip replacement. Consideration of local density variation of the femur may

similarly be taken into account based on CT measurements and associated local density derivation [121].

Additive manufacturing of various types of implants has gained in importance in parallel to the

rise to maturity and fame of the technology and as a means towards realizing the aforementioned

patient-specific adaptations. A recent study by Cronskär et al. has demonstrated a cost advantage of

35% when manufacturing a series of 7 hip stem implants using EBM-AM rather than conventional

(subtractive) manufacturing techniques [121]. The basis for implant layout, irrespective of the

production path chosen, is a CT scan and a derived 3D representation of the receiving bone (femur).

This established design approach is illustrated in Figure 9 and contrasted to its UDE-CBDM counterpart.

Figure 9. Typical design flow for implant layout according to Cronskär et al. [121] with additions

introduced by a PLM-data supported approach combining item and subgroup/population level

information. The diagram assumes PEID integration as integral part of the AM process.

What is currently still missing is the suggested combination of all three of the above elements,

i.e., the integration of sensors and sensor systems, the optimization of structural build-up, and the

realization via AM processes. In an implementation of the scenario proposed in the present study,

the initial design of an individual hip implant could be optimized according to information from a

general pool of data on hip implant use patterns. For a specific patient, information from the

respective database could be filtered based on the patient’s physical attributes and general

Figure 9.

Typical design ﬂow for implant layout according to Cronskär et al. [

121

] with additions

introduced by a PLM-data supported approach combining item and subgroup/population level

information. The diagram assumes PEID integration as integral part of the AM process.

What is currently still missing is the suggested combination of all three of the above elements,

i.e., the integration of sensors and sensor systems, the optimization of structural build-up, and the

realization via AM processes. In an implementation of the scenario proposed in the present study,

32100

Sensors 2015, 15, 32079–32122

the initial design of an individual hip implant could be optimized according to information from a

general pool of data on hip implant use patterns. For a speciﬁc patient, information from the respective

database could be ﬁltered based on the patient’s physical attributes and general knowledge about

use patterns. In the latter respect, personal data from other sources, such as body area networks or

smart watches, could also be drawn upon. Data sources would thus be both the UDE-CBDM and

the “other” data repositories included in Figure 8. The implant itself would be produced by means

of a suitable AM process including the integration of the required set of sensors, plus peripheral

electronics, energy supply systems (e.g., harvesting and storage-based) and wireless communication

hardware. Here, the remaining limitations of AM processes in terms of sensor integration come into

play. Information extraction could be permanent and patient centered, or performed during checkups.

In case of a pending replacement, the next generation implant could either be designed in direct

accordance with what the collected data suggests, or using predictions that derive trends from the

individual’s data. These data would originate from a larger database, from which, depending on

the speciﬁc case, comparable and matching data (e.g., age, gender and use pattern) could be derived.

The new implant would once again contain sensor systems for a further iteration. The aim, however,

would be to increase the timespan between replacements with this new design.

What makes this scenario a relatively simple one is the fact that the boundary conditions are

mostly static here, i.e., it is unlikely that the collected user data will suggest switching the principal

load cases that governs dimensioning. Instead, the accumulated data will only modify the loads

themselves, and thus change the design criteria merely on a parameter level, rather than on any

disruptive one. Reversing this argument, we have to conclude that optimization must be realized on a

very fundamental level in this case, like local variation of material properties, which can be achieved

via control of porosity levels. Such designed local porosity levels have already been the object of

intense study in the context of implants, and tools exist that are capable of optimizing them in relation

to the relevant load cases and with AM the method of choice for production [122].

For the maker of the implant, the implied business model is attractive, too, because the need

to retrieve and evaluate data from the previous device can be exploited to establish a customer

tie, to a mutual beneﬁt: What is sold with the ﬁrst implant is not only a physical product, but a

product with associated services, which open up an additional source of revenue for the vendor, and

ensure optimum product performance for the bearer. What the scenario still lacks in practice is the

technological capability of extending the data collection to any implant produced, and to do so in an

AM approach. Besides, the automated DaaS toolset and the connection between it and the collected

usage data plus associated information is not available yet. Well-matched optimization techniques, in

contrast, have already been demonstrated as stand alone solution.

4.2. Case Study 2: Robots as Intelligent Products—Agent-Based Cloud-Manufacturing Supported by

Product-Inherent Perception

This second case study outlines approaches for uniﬁed information processing architectures

that are suitable for additive and adaptive manufacturing based on a closed-loop sensor processing

approach, i.e., PLM by back propagation of inherent product perception. This approach combines

distributed data mining concepts (big data), sensor data processing, and the cloud computing

(Internet-of-Things) paradigm. Additive and adaptive cloud-based design and manufacturing

are attractive for the ﬁeld of robotics, targeting both industrial and service robots as well as

semi-autonomous carrier robots. In cloud-based manufacturing, the user of the product is integrated

in the distributed design and production process [

] through cloud-integrated computing and

storage solutions.

Robots can be considered as active, mobile, and autonomous data processing units, i.e., hardware

agents that are commonly already connected to computer networks and information processing

infrastructures. Robots use inherent sensing capabilities for their control and task satisfaction,

commonly delivered by integrated sensing networks with sensor preprocessing. These derive some

32101

Sensors 2015, 15, 32079–32122

inner state of the robot, for example, mechanical loads applied to structures of the robot or operational

parameters like motor power and temperature. The availability of the inner perceptive information

of robots enables the estimation of working and health conditions initially not fully considered at

design time. The next layer in the cloud-based adaptive manufacturing process can be the inclusion

of the products themselves delivering operational feedback to the current design and manufacturing

process, leading to a closed-loop evolving design and manufacturing process with an evolutionary

touch, shown in Figure 10 below. This evolutionary process adapts the product design, for example

the mechanical construction, for future product manufacturing processes based on a back propagation

of the perception information (i.e., recorded load histories, working and health conditions of the

product) collected by living systems at run-time. The currently deployed and running series of the

product enhances future series, but not in the traditional coarse-grained discrete series iteration. This

process can be considered as a continuously evolving improvement of the robot by reﬁning and

adapting design parameters and constraints that are immediately migrated to the manufacturing

process. A robot consists of a broad range of parts, most of which are critical for system reliability.

The most prominent failures are related to mechanical and electromechanical components, and are

typically caused by overload conditions at run-time under real conditions not considered or unknown

at initial design time. Furthermore, monitoring and proﬁling of the embedded software can be used

for improvement and revision processes in the software design, which can be correlated with other

perceptive data.

Sensors 2015, 15, page–page

process can be the inclusion of the products themselves delivering operational feedback to the

current design and manufacturing process, leading to a closed-loop evolving design and

manufacturing process with an evolutionary touch, shown in Figure 10 below. This evolutionary

process adapts the product design, for example the mechanical construction, for future product

manufacturing processes based on a back propagation of the perception information (i.e., recorded

load histories, working and health conditions of the product) collected by living systems at run-time.

The currently deployed and running series of the product enhances future series, but not in the

traditional coarse-grained discrete series iteration. This process can be considered as a continuously

evolving improvement of the robot by refining and adapting design parameters and constraints that

are immediately migrated to the manufacturing process. A robot consists of a broad range of parts,

most of which are critical for system reliability. The most prominent failures are related to

mechanical and electromechanical components, and are typically caused by overload conditions at

run-time under real conditions not considered or unknown at initial design time. Furthermore,

monitoring and profiling of the embedded software can be used for improvement and revision

processes in the software design, which can be correlated with other perceptive data.

Figure 10. Additive and adaptive Manufacturing with back propagation of sensing data using

mobile agents from robots to the design and iteration process resulting in continuous series

improvements.

The integration of robots as products and their condition monitoring in a closed-loop design

and manufacturing process is a challenge and introduces new distributed computing and data

distribution architectures in strong heterogeneous processing and network environments. One major

question to be answered is the sensing of meaningful condensed product condition information and

the delivery to the designer and factory. It is a similar issue that arises in the Internet-of-Things

domain. New unified data processing and communication methodologies are required to overcome

different computer architecture and network barriers. One common distributed and unified data

processing model is the mobile agent, a self-contained and autonomous virtual processing unit. The

mobile agent represents a mobile computational process that can migrate in the Internet domain and

in sensor networks as well. Multi-agent systems (MAS) represent self-organizing societies consisting

Figure 10.

Additive and adaptive Manufacturing with back propagation of sensing data using mobile

agents from robots to the design and iteration process resulting in continuous series improvements.

The integration of robots as products and their condition monitoring in a closed-loop design and

manufacturing process is a challenge and introduces new distributed computing and data distribution

architectures in strong heterogeneous processing and network environments. One major question to be

answered is the sensing of meaningful condensed product condition information and the delivery to

the designer and factory. It is a similar issue that arises in the Internet-of-Things domain. New uniﬁed

data processing and communication methodologies are required to overcome different computer

architecture and network barriers. One common distributed and uniﬁed data processing model is the

mobile agent, a self-contained and autonomous virtual processing unit. The mobile agent represents a

32102

Sensors 2015, 15, 32079–32122

mobile computational process that can migrate in the Internet domain and in sensor networks as well.

Multi-agent systems (MAS) represent self-organizing societies consisting of individuals following

local and global tasks and goals. A MAS is a collection of autonomous or semi-autonomous problem

solvers [

123

], often working proactively. This includes, for example, the coordination of information

exchange in the design and manufacturing process [

124

]. The agent’s action and behavior depends

on an abstract model of the environment described by ontologies. A MAS relies on communication,

negotiation, collaboration, and delegation with specialization including instantiation of new agents.

Agents are already deployed successfully for scheduling tasks in production and manufacturing

processes [

125

], and newer trends enquire into the suitability of distributed agent-based systems

for the control of manufacturing processes in a wider sense [

126

], facing not only manufacturing

itself, but maintenance, evolvable assembly systems, quality control, and energy management aspects,

ﬁnally introducing the paradigm of industrial agents meeting the requirements of modern industrial

applications. The MAS paradigm offers a uniﬁed data processing and communication model suitable

to be employed in the design, the manufacturing, logistics, and the products themselves.

The scalability of complex industrial applications using such large-scale cloud-based and

wide-area distributed networks deals with systems deploying thousands and up to a million agents.

But the majority of current laboratory prototypes of MAS deal with less than 1000 agents [

126

Currently, many traditional processing platforms cannot yet handle big numbers with the robustness

and efﬁciency required by industry [

127

128

]. In the past decade the capabilities and the scalability

of agent-based systems have increased substantially, especially addressing efﬁcient processing of

mobile agents.

There are programmable agent processing platforms that can be deployed in strong heterogeneous

network environments [

129

], ranging from single microchip up to WEB JavaScript implementations, all

being fully compatible on operational and interface level, and hence agents can migrate between these

different platforms. A distributed coordination and management layer enables the composition of

large-scale agent processing networks [

130

] including WEB browsers creating one big virtual machine.

Multi-agent systems can be successfully deployed in sensing applications, for example, structural load

and health monitoring, with a partition in off- and online computations [

131

]. Distributed data mining

and Map-Reduce algorithms are well suited for self-organizing MAS. Cloud-based computing, as a

base for cloud-based manufacturing, means the virtualization of resources, i.e., storage, processing

platforms, sensing data or generic information.

Traditional closed-loop processes request data from sources (products, robots) by using continuous

request-reply message streams. This approach leads to a signiﬁcantly large amount of data and

communication activity in large-scale networks. Event-based sensor data and information distribution

from the sources of sensing events, triggered by the data sources (the robots) themselves, can improve

the allocation of computational, storage, and communication resources signiﬁcantly and limit the need

for these.

A cloud in terms of data processing and computation is characterized by and composed of

‚ a parallel and distributed system architecture,

‚ a collection of interconnected virtualized computing entities that are dynamically provisioned,

‚

a uniﬁed computing environment and uniﬁed computing resources based on a service-level

architecture, and

‚

a dynamic reconﬁguration capability of the virtualized resources(computing, storage, connectivity

and networks)

Cloud-based design and manufacturing is composed of knowledge management, collaborative

design, and distributed manufacturing. Adaptive design and manufacturing enhanced with

perception delivered by the products incorporates ﬁnally the products in the cloud-based design

and manufacturing process.

32103

Sensors 2015, 15, 32079–32122

Agent Classes. The entire MAS society is composed of different agent classes that satisfy different

sub-goals and reﬂect the sensing-aggregation-application layer model: event-based sensor acquisition

including sensor fusion (Sensing), aggregation and distribution of data, preprocessing of data and

information mapping, search of information sources and sinks, information delivery to databases,

delivery of sensing, design, and manufacturing information, propagation of new design data to and

notiﬁcation of manufacturing processes, notiﬁcation of designer, end users, update of models and

design parameters. Most of the agents can be transferred in code frames with a size lower than 4kB,

and depends on the data pay-load they carry. At run-time, agents are instantiated from these different

classes, and agents can change to a subclass behavior depending on current sensing, goals, and their

inner state.

4.3. Case Study 3: Tailor-Made Shoes—Value Co-Creation Approach with Reactive Rubber 3D Printer

The present case study details a consumer product scenario (shoes and the shoe industry) studied

in some detail in the course of a publically-ﬁnanced project inspired by the needs of a local industry

and aiming at the validation of a smart factory concept that incorporates many features of UDE-CBDM

as discussed here.

Because of current trends in modern life and longevity, it has become common to wear shoes daily

for long periods. Consumers devote great concern to “foot comfort” or “shoes ﬁt feeling” in various life

scenes, such as health promotion, rehabilitation, running or other athletics, business use, etc. Rubber

is an important material used to develop shoes of various types to achieve comfort levels and/or

customer satisfaction.Kobe, the birthplace of the rubber industry in Japan, is still the country

s largest

base of chemical shoes despite damage caused by the Great Hanshin-Awaji Earthquake. Therefore it is

important for the Kobe area to realize and maintain the innovative capabilities of the rubber industry

to secure its footing in the global competition. The current study’s target is the athletic shoe industry

as the ﬁrst step to validate a smart factory concept with user involvement.

The value co-creation loop in our project is presented in Figure 11. The loop consists of four major

processes: Analysis, Design, Operation and Application.

Sensors 2015, 15, page–page

acquisition including sensor fusion (Sensing), aggregation and distribution of data, preprocessing of

data and information mapping, search of information sources and sinks, information delivery to

databases, delivery of sensing, design, and manufacturing information, propagation of new design

data to and notification of manufacturing processes, notification of designer, end users, update of

models and design parameters. Most of the agents can be transferred in code frames with a size

lower than 4kB, and depends on the data pay-load they carry. At run-time, agents are instantiated

from these different classes, and agents can change to a subclass behavior depending on current

sensing, goals, and their inner state.

4.3. Case Study 3: Tailor-Made Shoes—Value Co-Creation Approach with Reactive Rubber 3D Printer

The present case study details a consumer product scenario (shoes and the shoe industry)

studied in some detail in the course of a publically-financed project inspired by the needs of a local

industry and aiming at the validation of a smart factory concept that incorporates many features of

UDE-CBDM as discussed here.

Because of current trends in modern life and longevity, it has become common to wear shoes

daily for long periods. Consumers devote great concern to “foot comfort” or “shoes fit feeling” in

various life scenes, such as health promotion, rehabilitation, running or other athletics, business use,

etc. Rubber is an important material used to develop shoes of various types to achieve comfort levels

and/or customer satisfaction.Kobe, the birthplace of the rubber industry in Japan, is still the

country's largest base of chemical shoes despite damage caused by the Great Hanshin-Awaji

Earthquake. Therefore it is important for the Kobe area to realize and maintain the innovative

capabilities of the rubber industry to secure its footing in the global competition. The current study’s

target is the athletic shoe industry as the first step to validate a smart factory concept with user

involvement.

The value co-creation loop in our project is presented in Figure 11. The loop consists of four

major processes: Analysis, Design, Operation and Application.

Figure 11. Proposed value co-creation loop.

Digital human technology is applied to conduct comprehensive analyses based on market data.

User motion data and biological data are obtained precisely from the user market with sensing

devices such as motion capture or CT scan equipment. Then interactive communication design is

done based on the analytic data. Communication is realized with several smart devices under an IoT

environment. Service applications connect producers and users, who can send attributes or

Figure 11. Proposed value co-creation loop.

Digital human technology is applied to conduct comprehensive analyses based on market data.

User motion data and biological data are obtained precisely from the user market with sensing devices

such as motion capture or CT scan equipment. Then interactive communication design is done based

on the analytic data. Communication is realized with several smart devices under an IoT environment.

32104

Sensors 2015, 15, 32079–32122

Service applications connect producers and users, who can send attributes or preferences to producers,

who provide tailor-made product design services including athletic functions and surface design.

The diversity of products is increased greatly by tailor-made production. This implies that the

smart factory technology for mass customization production is operated from the design to operation

phase as process innovation. Distributed and autonomous production mechanisms are implemented

transparently within the smart factory to correspond with several levels of user satisfaction in an

IoT environment. AM has a major role in this concept, as it is mandatory to achieve the required

customization levels: We can attempt to invent a new reactive 3D printer for rubber materials to

attain tailor-made rubber products in the production phase. Rubber products with functionally

graded materials are realized with embedded pressure-sensitive sensors. Consequently, any kind of

users’ personal comfort related to shape and function of athletic shoes (i.e., inner/mid/outer soles) is

attainable with the reactive rubber 3D printer.

Rubber products can be integrated into the soles of running shoes of professional athletes (lead

users). Then the information collected through the sensor under an IoT environment should be

applied to shoe design for the more casual runners (semi-lead users), and eventually for mass users in

society. The level of each phase in the loop differs according to the loop hierarchy. Design intellectual

transformation from lead users to mass users is executed via a three-layered-business system.

The entire project design, particularly the iterative data collection and the tailoring process for

customers, involves “service design”. Subconscious user delight is extracted using the proposed

continuous interactions amongst users of several kinds and producers in the business system. User

centered-innovation is achieved in our project with reactive rubber 3D printing technology.

A new smart factory including the concept of “value in use” is realized in this research project.

As an example of our development, our smart factory structure for mass customization of the athletic

shoe industry is presented in Figure 12.

Sensors 2015, 15, page–page

preferences to producers, who provide tailor-made product design services including athletic

functions and surface design.

The diversity of products is increased greatly by tailor-made production. This implies that the

smart factory technology for mass customization production is operated from the design to

operation phase as process innovation. Distributed and autonomous production mechanisms are

implemented transparently within the smart factory to correspond with several levels of user

satisfaction in an IoT environment. AM has a major role in this concept, as it is mandatory to achieve

the required customization levels: We can attempt to invent a new reactive 3D printer for rubber

materials to attain tailor-made rubber products in the production phase. Rubber products with

functionally graded materials are realized with embedded pressure-sensitive sensors. Consequently,

any kind of users’ personal comfort related to shape and function of athletic shoes (i.e.,

inner/mid/outer soles) is attainable with the reactive rubber 3D printer.

Rubber products can be integrated into the soles of running shoes of professional athletes (lead

users). Then the information collected through the sensor under an IoT environment should be

applied to shoe design for the more casual runners (semi-lead users), and eventually for mass users

in society. The level of each phase in the loop differs according to the loop hierarchy. Design

intellectual transformation from lead users to mass users is executed via a three-layered-business

system.

The entire project design, particularly the iterative data collection and the tailoring process for

customers, involves “service design”. Subconscious user delight is extracted using the proposed

continuous interactions amongst users of several kinds and producers in the business system. User

centered-innovation is achieved in our project with reactive rubber 3D printing technology.

A new smart factory including the concept of “value in use” is realized in this research project.

As an example of our development, our smart factory structure for mass customization of the

athletic shoe industry is presented in Figure 12.

Figure 12. Proposed smart factory structure.

A smart factory encompasses all business layers in the industrial value chain including

consumers with IoT environment. Most software modules are integrated in manufacturing

management and control, such as ERP, SCM, MES, and SFA. Then a multi-agent based intelligent

CPS system, named a Real-Virtual fusion System [132], is implemented into the smart factory to

realize distributed and autonomous factory management and control. Thus apart from the

fully-automated cloud-based design and optimization system, which forms the core of the suggested

Figure 12. Proposed smart factory structure.

A smart factory encompasses all business layers in the industrial value chain including consumers

with IoT environment. Most software modules are integrated in manufacturing management and

control, such as ERP, SCM, MES, and SFA. Then a multi-agent based intelligent CPS system, named

a Real-Virtual fusion System [

132

], is implemented into the smart factory to realize distributed and

autonomous factory management and control. Thus apart from the fully-automated cloud-based

design and optimization system, which forms the core of the suggested DaaS system, all aspects of

theUDE-CBDM concept proposed are reﬂected in this case study, including the gathering of usage data

through lead users. Effectively, this constitutes the deﬁnition of a distinct user group, the data gathered

32105

Sensors 2015, 15, 32079–32122

by which is intended to support optimization tasks for additional user groups. Contrary to the full

UDE-CBDM concept, secondary and ternary user groups will not be drawn on to a similar degree,

i.e., no sensor integration is foreseen in their case. Of speciﬁc interest, however, is the fact that the

smart factory concept does include user feedback, and even indirectly so, through the establishment

of communication links between users and producers, potentially on social network level or using

similar concepts, and the analysis of this communication.

5. Discussion

As presented above, the UDE-CBDM scenario deﬁnes and demonstrates a trend affecting several

stakeholders. In the present section, a critical reﬂection of assumptions made in this context and their

implications is presented using the main stakeholder’s perspectives (i.e., designer, producer, user

and regulatory or public bodies) as structuring element. In addition, general challenges related to

privacy and security concerns are highlighted. Figure 13 denotes these various points of view, adding

technologies and infrastructure as their common foundation. The discussion itself will not be dogmatic

in the sense that only a full realization of the UDE-CBDM scenario is considered worth discussing. In

practice, several prior, intermediate levels can be envisaged.

Sensors 2015, 15, page–page

DaaS system, all aspects of theUDE-CBDM concept proposed are reflected in this case study,

including the gathering of usage data through lead users. Effectively, this constitutes the definition

of a distinct user group, the data gathered by which is intended to support optimization tasks for

additional user groups. Contrary to the full UDE-CBDM concept, secondary and ternary user groups

will not be drawn on to a similar degree, i.e., no sensor integration is foreseen in their case. Of

specific interest, however, is the fact that the smart factory concept does include user feedback, and

even indirectly so, through the establishment of communication links between users and producers,

potentially on social network level or using similar concepts, and the analysis of this

communication.

5. Discussion

As presented above, the UDE-CBDM scenario defines and demonstrates a trend affecting

several stakeholders. In the present section, a critical reflection of assumptions made in this context

and their implications is presented using the main stakeholder’s perspectives (i.e., designer,

producer, user and regulatory or public bodies) as structuring element. In addition, general

challenges related to privacy and security concerns are highlighted. Figure 13 denotes these various

points of view, adding technologies and infrastructure as their common foundation. The discussion

itself will not be dogmatic in the sense that only a full realization of the UDE-CBDM scenario is

considered worth discussing. In practice, several prior, intermediate levels can be envisaged.

Figure 13. The main stakeholders and perspectives and their relation to each other.

5.1. Designer’s Perspective

With the notion of products being continuously adapted depending on how they are actually

used, designers have to acquire additional skills and knowledge in several different domains.

Selected examples are explained in the following.

First, the ability of production to adapt to and thus facilitate variation from product item to

product item requires that products are designed with extensive flexibility in mind [133]. A similar

development was necessary for the transition from mass production towards mass customization,

where the intense use of modular product architectures and parametric design allows customers to

select those system configurations that would fit their purpose best [134]. For reason of such

characteristics, parametric design has been linked to both simulation and design optimization

[135,136] and digital, i.e., additive manufacturing [137]. In principle, however, it remains a way to

Figure 13. The main stakeholders and perspectives and their relation to each other.

5.1. Designer’s Perspective

With the notion of products being continuously adapted depending on how they are actually

used, designers have to acquire additional skills and knowledge in several different domains. Selected

examples are explained in the following.

First, the ability of production to adapt to and thus facilitate variation from product item to

product item requires that products are designed with extensive ﬂexibility in mind [

133

]. A similar

development was necessary for the transition from mass production towards mass customization,

where the intense use of modular product architectures and parametric design allows customers

to select those system conﬁgurations that would ﬁt their purpose best [

134

]. For reason of such

characteristics, parametric design has been linked to both simulation and design

optimization [135,136]

and digital, i.e., additive manufacturing [137]. In principle, however, it remains a way to describe, on

different levels, the geometry of a product as function of a set of parameters that can be treated as

variables for creating design variants [

138

]. The present UDE-CBDM approach needs ﬂexibility

on a much ﬁner level. Consider a design that is controlled by the ability to withstand certain

32106

Sensors 2015, 15, 32079–32122

mechanical loads as main requirement. Typically, information derived from usage data will “reﬁne”

these requirements, suggesting limited change to a preceding or to the initial “root design” set to

achieve improved performance—e.g., weight-speciﬁc strength. Modularity alone will not sufﬁce to

ﬁne-tune such a design, speciﬁcally if it is already nearing saturation on a generation-to-generation, or

rather item-to-item, performance increase curve. Effectively, parametric design needs to be extended to

incorporate additional features such as local material properties—provided that these can be accessed

by the manufacturing process chosen. AM has this potential: Developments like the so-called Digital

Materials approach [

139

] are currently being commercialized by companies like Stratasys. In this

latter case, under the trade name PolyJet, a material jetting technology based on inkjet printing using

multiple print heads is employed. Within the boundary conditions of the process in general, this

strategy permits voxel-to-voxel change of materials and thus properties, allowing design of materials

e.g., with tailored mechanical performance [

140

141

]. Similarly, process classes like directed energy

deposition or 3D printing as example of the binder jetting approach may develop in this direction, as

they allow either switching between materials (DED) or controlled modiﬁcation through additives

for which the binder serves as carrier medium [

]. Integrating such local material behavior in the

design workﬂow will afford several changes, including the representation of the product as a CAD

model and the translation of the latter to a production oriented format: For this purpose, the standard

solution in AM, the STL ﬁle format, is not suitable anymore [

140

]. In terms of simulation-based

optimization, established approaches like shape or topology optimization likewise fail to capture the

added complexity. Further advancement of methods like Multi-Phase Topology Optimization (MPTO)

introduced by Burblies et al. can help close this gap: The methods provides optimum distributions

of porosity levels within mechanically loaded components [

122

] and has been demonstrated to be

transferable to AM-based implementations. The envisaged DaaS toolset, in its optimization and CAD

modelling capabilities as well as in its output to the manufacturing system, will have to apply such

features, and allow their modiﬁcation in response to usage data input.

Secondly, with an increasing ﬂexibility in design, an extensive knowledge of the dependencies

among different system components is needed. Making one part ﬂexible might lead to immediate

or long-term consequences for related elements of the whole system. The same holds true for solely

component-centered optimization which ignores interdependencies within the overall system and

may thus lead to performance reduction or even premature failure on system level.

Thirdly, a critical aspect of adaptive products is that adaptations must meet high quality standards

at any moment of the product life. In contrast to biological systems, that perform a trial-and-error

approach of which death is just a necessary and accepted concept, technical systems must avoid costly

failures. In order to guarantee such levels of quality, testing procedures have to be applied during

product design and development. However, physical testing fails to fulﬁl the basic demands of our

suggested approach for two reasons: Since it requires production of prototypes and availability of

test facilities it contradicts the notion of individually optimized products on a level of principle, and

it eliminates much of the advantage in lead time reduction that AM as production method offers.

Furthermore, it would limit the choice of possible production sites to those that have the right test

equipment at hand, and would thus cancel out the envisaged decentralized production option. For

these reasons, affordable personalized and individually optimized products as foreseen in this paper

must make extensive, if not exclusive, use of virtual testing methods and environments.

Fourthly, taking usage information into account during design decisions requires knowledge

in representation and interpretation of the information. Representation is bound to data analysis

techniques ranging from basic statistics to complex data mining approaches. In using the gathered

data, distinctions have to be made regarding the level on which optimization is implemented. The

following main categories can be identiﬁed:

Optimization on individual/item level: A follow-up product serving the same purpose for the

same user can be optimized to meet an adapted, personalized set of requirements.

32107

Sensors 2015, 15, 32079–32122

Optimization on subgroup level: Collection of PLM data may reveal certain recurring types of

usage, allowing identiﬁcation of and thus product optimization for certain user subgroups. As in the

previous case, this implies that additional, deﬁned sets of requirements can be formulated on subgroup

level that deviate from and/or add to the generalized set.

Optimization on population level: On a general level, collection of PLM data will establish a

major step forward in terms of usage patterns and thus help to focus and/or boil down the set of

requirements all individuals/items of a product (on product deﬁnition in this context, see statements

below) have to conform to.

Distinction between these levels needs to be reﬂected in the data analysis techniques employed.

An additional aspect worth mentioning in this context is the fact that in a scenario with item-level

optimization, all data will be gathered by slightly deviating individual products. Thus it is necessary

to distinguish the lessons to be learned from the usage data. One part concerns lessons about the use

case (e.g., involved actors and product functions), and a second part concerns those about the actual

product item that gathered the respective data set. It is furthermore important to derive rules that

allow combination of data originating from different product items, and thus different expressions of a

common “root design”. Capabilities of this kind would be associated to the interfacing and mapping

element speciﬁcally highlighted in Figure 8.

More generally speaking, the interpretation of information is a matter of human experience and

understanding of context. Advances in data analytics techniques created a new profession, the data

scientist [

142

]. In order to learn from usage data, designers must collaborate with data scientists to

ﬁnd appropriate answers for design problems within the data. Typical examples are the search for

new requirements or the validation of existing requirements typically documented before starting

to create or alter a product’s design. Besides technical and collaboration challenges related to the

analysis of usage information, it is also necessary for designers to understand the limitations of data

analytics. An important concept that must be considered in this respect is information quality [

143

Any piece of usage information is created within a certain context and used for one or more speciﬁc

purposes. In the case of sensor data, a speciﬁc class and make of sensor is operated to acquire the

raw data. The context of each sensor (e.g., whether it was calibrated or not) is needed to correctly

interpret the resulting data. Without this information, designers and data scientists must be careful

concluding on the data. In addition, the understanding of complex usage patterns from sensor data

may require complementary information, such as user-authored information in discussion forums,

social networking services, maintenance reports and product reviews. Besides that, information from

complementarily used products should be taken into account to gain a better understanding of user

problems. The improvement of shoes, for instance, might need to take data from a backpack (e.g., load

during movement) or umbrella into account (e.g., local weather). Such complementary information

sources add to the context of a speciﬁc design decision and may thus reduce uncertainty—though here,

too, quality of information has to be taken into account.

All the above needs to be considered beforehand and designed into any future system capable of

automating the design optimization process, thus taking central decisions in this ﬁeld out of the hands

of human designers. Automated design must evolve to deliver valid solutions fast and in a directly

applicable manner based on the growth and evolution of the available usage data. This implies ﬁrst

the connection of the relevant tools applied in the design process to these data, secondly the feeding of

these with knowledge and rules on AM processes. Today, the latter is still seen as a major obstacle to

the distribution of AM technologies even in a conventional design and development process, since

many of the speciﬁc rules of “design for AM” have not been formulated, let alone spread widely yet.

5.2. Producer’s Perspective

Before discussing the producer’s perspective in the following, the difference from the designer’s

perspective has to be elaborated. In the described scenario, this differentiation is more ﬂuid than is

known from the related traditional structures in industry. In UDE-CBDM, design and manufacturing

32108

Sensors 2015, 15, 32079–32122

is a continuous effort, which requires exchange of information and transparency throughout the

value-chain. Thinking this even further, the “producer” might even be the user him/her-self,

employing decentralized AM capabilities at home or in the local Maker-Space. In such a case, the

“traditional” producers responsibilities may be reduced to supplying the material (if that is the case at

all), or to serving as an intermediary supplying the parameters/speciﬁcation for the AM process at the

users site—or his role might even become completely obsolete.

However, this extreme example is still far from reality. Industrial AM equipment is still rather

costly, not only regarding the tools themselves but also the service contracts required to keep the system

in a working condition. Therefore, the role of the producer is valid and an important component of the

value chain in this vision. The re-shufﬂing of the latter will also open up new business opportunities

both for the producer, and for third parties that have access to the usage data that product and product

groups supply. Figure 7 illustrates some of the potential connections in this respect.

The basic assumption is that usage data gathered by the product can either stay within the

maker’s realm, where it is accompanied by data associated with the manufacturing of the part, i.e.,

BoL data, or it can be passed on into a “public” cloud, naturally assuming that all necessary steps

for ensuring security and privacy (see Section 5.5) have been taken. The availability of such data

facilitates several potential product-related services, which may be offered by the maker or by third

parties if these have legal access to the data: The fundamental business case for the original producer

of a part could thus be the provision of a (personalized or non-personalized) optimization service to

the customer. Further product-related services may include maintenance, which could be handled

by the maker, or left to third parties. In the latter case, depending on the legal situation in that

respect, the producer might enter into a business relationship with the services provider, granting him

access to standard or extended product-related data. In parallel, the service provider could use other

cloud-based resources to add to the product-speciﬁc data and thus enhance or extend his services. A

special case is the full-ﬂedged scenario assuming an automated design engine, the DaaS toolset, that

creates the subsequent product generation automatically (note that the optimization service could also

be envisaged following a conventional design approach), once again making use of available data on

the preceding generations. Here, redesign could be a service offered via the cloud, with not so much

the primary, but potentially secondary producers acquiring access to the redesign and optimization

tool, which might be offered by a software developer. Since the original maker would not be involved

here, Beginning of Life (BoL) data as a additional source of information would probably be lacking,

however, for the new product entity, its new, originally secondary, producer would assume the role of

primary maker, with all the facets this entails according to Figure 14.

Sensors 2015, 15, page–page

Figure 14. An overview of data flows between stakeholders integrating the cloud and highlighting

possibilities of cloud-based product-related services.

Being responsible for transforming the virtual design in a physical product, the producer’s

responsibility includes ensuring that the manufacturing processes deliver the required quality as it is

the case today. Due to the flexibility of AM and the availability of the design in the required format,

the impact of the producers’ expertise will shift slightly towards supporting the designers from an

early stage on. This support includes but is not limited to: Design for Manufacturability (DfM),

choice of available material, dimensioning (based on available equipment).

Besides, defining the new role of the producer requires answering a multitude of open

questions. Decentralization of production, including item-to-item adaptation of design will require

formulations that clearly define responsibilities along this process chain: If a product fails on

item-level, who is to blame? Will it always be possible to associate the cause of failure with either

root design, collection of usage data, interpretation of it, transfer of data (cyber security aspects),

product optimization or production? In this context, we need to take into account that it might not be

practically feasible to establish an answer on a case-by-case level. Thus there might be a need for

generalized rules, which would mean that the relationship between, for instance, designer,

manufacturer and service provider may have to be re-thought and cast into a working formalism, be

it on a contract or licensing level, or another suitable mode.

Finally, the producer, bound by current regulations, may also find him-/herself responsible for

the recycling of the products after they reach the end of their service life. As each product is unique

in the most advanced scenario, this might add additional challenges. These challenges include

dismantling in case sensors are implemented (at different, item-specific locations), hazardous

materials, precious metals and others.

5.3. User’s Perspective

For a user, our scenario has several benefits in store, not the least among them the chance to

obtain optimized follow-up products adapted to his personal needs. There is, however, no rose

without thorns, and in the present case, to realize these advantages, the user may have to become an

active partner of the producer. Currently this must seem an uncommon relationship thinking of

everyday goods like shoes, and it must be taken into account in this context that even if the

individual task may seem negligible from a single product perspective, it will accumulate the more

candidate products the user chooses to maintain in this same way. The actual tasks the user might be

entrusted with may include the read-out of data, the added, personal evaluation of product

Figure 14.

An overview of data ﬂows between stakeholders integrating the cloud and highlighting

possibilities of cloud-based product-related services.

32109

Sensors 2015, 15, 32079–32122

Being responsible for transforming the virtual design in a physical product, the producer’s

responsibility includes ensuring that the manufacturing processes deliver the required quality as it is

the case today. Due to the ﬂexibility of AM and the availability of the design in the required format,

the impact of the producers’ expertise will shift slightly towards supporting the designers from an

early stage on. This support includes but is not limited to: Design for Manufacturability (DfM), choice

of available material, dimensioning (based on available equipment).

Besides, deﬁning the new role of the producer requires answering a multitude of open questions.

Decentralization of production, including item-to-item adaptation of design will require formulations

that clearly deﬁne responsibilities along this process chain: If a product fails on item-level, who is to

blame? Will it always be possible to associate the cause of failure with either root design, collection

of usage data, interpretation of it, transfer of data (cyber security aspects), product optimization or

production? In this context, we need to take into account that it might not be practically feasible to

establish an answer on a case-by-case level. Thus there might be a need for generalized rules, which

would mean that the relationship between, for instance, designer, manufacturer and service provider

may have to be re-thought and cast into a working formalism, be it on a contract or licensing level, or

another suitable mode.

Finally, the producer, bound by current regulations, may also ﬁnd him-/herself responsible for the

recycling of the products after they reach the end of their service life. As each product is unique in the

most advanced scenario, this might add additional challenges. These challenges include dismantling

in case sensors are implemented (at different, item-speciﬁc locations), hazardous materials, precious

metals and others.

5.3. User’s Perspective

For a user, our scenario has several beneﬁts in store, not the least among them the chance to obtain

optimized follow-up products adapted to his personal needs. There is, however, no rose without

thorns, and in the present case, to realize these advantages, the user may have to become an active

partner of the producer. Currently this must seem an uncommon relationship thinking of everyday

goods like shoes, and it must be taken into account in this context that even if the individual task may

seem negligible from a single product perspective, it will accumulate the more candidate products

the user chooses to maintain in this same way. The actual tasks the user might be entrusted with may

include the read-out of data, the added, personal evaluation of product performance and performance

change, as well as maintenance tasks that are aimed at ensuring the quality of data to stay on a constant,

high level throughout the product’s lifetime.

A side aspect of the implied personalization of products may be the impossibility of allowing them

to be used by other people aside from the owner. Both in private life and in a sharing or collaborative

consumption economy, which currently seems to evolve in several ﬁelds, this may prove a restriction

felt severely by many users.

Finally, the big issues when it comes to usage data are privacy and ownership of data: These

will be treated in more detail in Section 5.5, since they touch upon the realm of users and other

stakeholders alike.

Considered along the above lines, the user is the end user or customer that will directly utilize

the ﬁnal product—wear the shoes, if we refer to case study 3, or the smart implant, if we consider

case study 1. In practice, the interface between this user and the cloud-based services that lead to his

personal product might often be provided by a dedicated service provider. For both, the customer

or end-user and for any intermediary, it is very important to gain an idea of the reliability of the

manufacturing chain behind the interface to service provider (as potential intermediary), or the

cloud-based services themselves. In a conventional production scenario, the end-user would base

his trust e.g., on the reputation of a brand, i.e., an OEM. In CBDM in general, and thus also in its

UDE-CBDM variant, the manufacturing chain is much more susceptible to change, as the model

implies choice of manufacturing opportunities based on factors like availability and price raised almost

32110

Sensors 2015, 15, 32079–32122

in real time at the instant an order for a product is placed. Thus instead of ﬁxed, well established

supplier relations, a constant reconﬁguration of the chain is possible from single order to another single

order. In such an environment, subjectively building up trust is rendered difﬁcult if not impossible.

In consequence, as Li et al. point out, some formal, more objective mechanism for evaluation of

trustworthiness needs to be established [144].

5.4. Regulatory or Public Bodies

In its practical implementation, the ﬁnal consequence of our approach will be a certain loss

of meaning for the term product series. Many of the above statements imply that products can be

identiﬁed on population level as belonging together through a set of measurable—and this means

measurable on the product—parameters that for this population take on the same value. This is

certainly true in conventional manufacturing, when e.g., the same die is used to cast a series of wheels.

However, according to our present approach, such commonalities are lost because in the extreme

case, a redesign process is initiated prior to the manufacturing of each individual and based on the

usage data available at that speciﬁc moment in time: For a product made a few minutes later, the

data base may already have changed. What constitutes a product in this world could then only be

a generalized description of purpose, i.e., of the application scenario, and a set of equally general

common requirements. Neither of these would sufﬁce to actually explain all the speciﬁc properties of

an individual product, because these would be based partly on the general requirements, and partly

on individual- or subgroup-related ones. It would thus only be possible to speak of a product in the

broader sense of a group of objects built to meet a common class of general requirements, and having

a common “root design”.

Considering the practical implications of the above conjecture, it is obvious that under such

circumstances, the concept of type approval and homologation cannot be maintained. On the other

hand, it is similarly obvious that it is impossible for economic reasons to seek approval on a single

item level.

Here, a solution comes to mind which has already been discussed in terms of its role in the design

process in Section 5.1: If physical testing is out of question, virtual testing must take its place. This,

however, does not eliminate the practical issue that not every individual product entity can be virtually

tested by a certifying authority. Thus wherever certiﬁcation of products is an issue, this means that the

virtual test procedures themselves must ﬁrst be acknowledged and certiﬁed, to guarantee their correct

application, and to allow the results to be accepted as proof of the product’s adherence to the relevant

rules and standards. Similarly, it might be possible to evaluate the optimization procedures that govern

entity-to-entity product evolution. In this case, a conventional certiﬁcation of a “root design” could be

implemented and “measures of change” developed: Their limiting values would then deﬁne a corridor

the violation of which by product characteristics would set the mark for recertiﬁcation, turning the

then-current product design stage into the new root design with respect to certiﬁcation. To make such

a process calculable for the producer, predictive tools might be needed allowing an estimate of levels of

change to be expected within a given period of time, in order to identify the tipping points in advance

and prepare for the recertiﬁcation effort in time.

The fundamental assumption in the discussion above is that certiﬁcation authorities and

regulatory bodies have to deal with a single, combined designer and producer. Needless to say,

the issue becomes more complex once the elements of decentralized production and cloud-based

optimization are added. Design evolutions would then occur independent of the “root designer”,

which would raise additional liability issues. Once again a step towards a solution could be to certify

the methods and their use, and not the product, or rather product state. However, as already mentioned

in Section 4.2, even if this approach proved possible, liability issues along the chain or rather network

of designer, initial producer, optimizer, secondary producer and customer would still need to be

clariﬁed, and accepted, and potentially globally enforced rules in this respect laid down.

32111

Sensors 2015, 15, 32079–32122

5.5. Privacy and Security

Another general challenge beyond our speciﬁc scenario is the question of data ownership and

privacy, which has been introduced already in Section 4.3. Usage data is by deﬁnition generated

when the product is used, and by the user. Our model assumes, for example, that the original

designer of the product, or rather those group that provides the optimization service, has full

access to the collected data, or at least the information derived from it. This access may or may

not include additional information about the user of a speciﬁc product entity, again with a possible

differentiation in personal/individual or user group data. Access to this data is likely to allow its

evaluator to derive several conclusions on user or user group that go far beyond the needs for product

optimization. Thus the user has to be convinced that he/she should share his/her data with the

manufacturer/designer—either for his personal (item-level approach), or for the mutual beneﬁt of

broader user communities (subgroup and/or population level approach). This entails legal issues,

like misuse of sensitive data etc. However, as this is a strongly discussed topic today, there is a large

chance that this issue will be solved one way or another within the foreseeable future. In any case, it is

clear that a legal framework has to be created that clearly deﬁne the involved parties’ rights as well as

their limitations.

Besides privacy, cyber security is becoming more and more of a concern both in the area of

networked smart systems and objects (from cars to IoT) and in that of Additive Manufacturing. Here,

we combine both aspects. New issues include questions such as product identiﬁcation and protection

against plagiarism, but also the issue of assuring that data has not been corrupted in some way or

another on its path from designer to producer, with potentially critical consequences for product safety,

to give but one example. Consistency of data thus needs to be ensured.

An increasing degree and complexity of networking and information exchange between a large

number of computers, mobile devices, sensor networks, individual smart sensors, products and

machines induce new challenges in the handling of privacy of data, privacy of users, authorization,

and authentication in a mostly anonymous Internet-of-Things (IoT) domain. New concepts and

methodologies are required for binding and granting access rights to data, creating access roles based

on application rather than on user level, traditionally managed by higher communication layers like

WEB servers or databases providing account services. Considering consumer products that deliver

lifecycle data, the users and their location are mainly unknown. By enabling usage data delivery from

products to manufacturers these anonymous users get a virtual identity encapsulating private user

data, for example, their geographic location (that can be accurately determined by network services

and fusing of other data), the usage behavior, and so on. In the proposed design and manufacturing

clouds, which are integrated in the vulnerable Internet, Cyber Attacks can signiﬁcantly disturb the

processes or spy sensitive or conﬁdential data. If sensor, product, and user data is autonomously

transferred, for example, by mobile agents, the data become self-contained units that can be processed

on any platform, ranging from embedded systems up to servers. It is relatively easy for intruders

to include Man-of-the-Middle attacks to retrieve and spy secured data, and in service-orientated

processing environments Denial-of-Service (DoS) attacks can prevent an overall system operation.

Currently the security issues concerning stealing data or attacking services are not addressed properly

in the IoT community. Security issues are related to the user (data producer) and service provider (data

consumer) side, though both sides have different goals and constraints in mind.

Looking at cyber security speciﬁcally from a producer’s perspective, as Lu et al. point out, it must

be taken into account that the importance of intangible knowledge assets, in other words, of data, for

organizational success in industry is clearly on the rise, while at the same time, the accessibility of data

and thus vulnerability of the respective assets is increasing with any bit of data that is mirrored in the

cloud. The dilemma for the producer is between the wish to raise the potential of cloud-based services

and keep track of his competitors in that respect, and the risks that goes with moving business into the

cloud. Consequently, dedicated strategies for ensuring cyber security are mandatory [145].

32112

Sensors 2015, 15, 32079–32122

6. Conclusions and Outlook

In the present work, we have outlined the characteristics of a new manufacturing paradigm

designated as Usage Data-Enhanced Cloud-based Design and Manufacturing (UDE-CBDM). We have

explained why we believe

‚ usage data collection and analysis, facilitated by

‚ sensor integration in

‚ additive manufacturing and fed into

‚ automated design processes

may develop into the main foundations of our approach, and along which paths this needs to happen.

We have furthermore discussed the role of cloud-bases services in this context, and the potential these

offer, e.g., in terms of new business models associated to the suggested alternative organization of the

product development and manufacturing process. In considering the above research domains, we

notice that many of our concept’s prerequisites are either there or under development, and that to

a signiﬁcant degree, the challenge of realizing UDE-CBDM translates into the challenge of correctly

arranging and interfacing these building blocks.

Within the PLM domain, new frameworks and methodologies are surfacing at ever-increasing

rate. Especially item-level based approaches utilizing the potential of usage information are rather

popular [

146

]. Sensors neatly integrated in the products in question are the initial prerequisite

without which the manufacturing paradigm we have outlined above could not ﬂy. Similarly, applying

this information for product optimization needs a production process characterized by highest

ﬂexibility and lowest lead times. From the aforesaid, the need for AM in this context and its crucial

role on a technological level is obvious: Continuous redesign and individualized production is

only possibly if adaptation to design changes is next to instantaneous and investment costs are

not linked to an individual product design, as is the case when moulds and tooling are required.

AM, and at present only AM, can successfully relinquish these obstacles. Currently, however, the

3D printer that generates an engineering component with integrated sensor systems in a single

step or setup still has to be designed, even though its predecessors are available on lab-scale, as is

documented e.g., by

Espalin et al. [80]

. Neat integration of sensors and sensor systems thus needs to

be further advanced to the level of material-integrated intelligent systems and against the background

of AM-based production.

The drive that AM adds to the vision is best understood when comparing it to earlier models

envisaging an evolutionary approach to product design: A good example in this respect is the

gentelligent design approach formulated by Lachmayer et al. [

147

]: Here, evolution is considered

on a generation-to-generation level, with continuity upheld mainly with respect to collection of

PLM data. This strategy thus represents a cut-out of what has been designated the Autogenetic

Design Theory (ADT) by Vajna et al. [

148

]: “Evolution means gradual development, permanent

adaptation, and continuous optimization towards an aim that may also change itself during the

evolution [

. . .

]. Additionally, dynamic starting conditions, boundary conditions, and constraints

inﬂuence natural evolution, which is exactly the same as in any design” [

148

]. The objective of ADT

is to understand the ruling principles behind this scenario from an engineering point of view and

implement them in product development processes to facilitate a comparable, continuous evolution in

product design [148].

It is worth considering, at this stage, that the present concept, when seen as a manifestation

of ADT, is in some ways more powerful than its biological equivalent, and may thus approach an

envisaged optimum stage signiﬁcantly faster. In biological evolution, the selector is life itself, and

thus the underlying timescale is that of life and reproduction: The ability of a new individual to

survive and multiply in its environment provides directions for changes to become dominant in

future generations. Variation of individuals is achieved by mutation, but even more so by more

or less controlled combination of the genetic background of two parents. Thus success stories of

32113

Sensors 2015, 15, 32079–32122

survival from the top down to the root of the parents’ family trees are covered, while other trees

rooted in the same forest are neglected. In contrast, in our vision, redesign prior to production of

a new item can in principle draw an all the data available at that moment in time—data from all

the trees in this and other forests. In practice, this means that the scope of usage data will rapidly

outgrow the possible experience of a single individual. With availability of information accelerated

by accumulation from several sources, product optimization may progress faster, as compliance and

performance improvements will be veriﬁed in the virtual rather than the real world, through evolved

simulation and optimization tools as part of the suggested DaaS system. Besides, if customer or

customer group are known, and thus a detailing of usage patterns is available, optimization can be

customized by basing it on a prognosis of this speciﬁc item’s most probable lifecycle.

The implication that the product development process will never reach an end will afford

investment in automation of the design process. The expertise required in product development

as a business case may thus shift to different foci. This shift will require moving towards a higher-level

understanding of formal product development processes which in their practical implementation on

product level could then be performed automatically. A major challenge in this respect is the mapping

between usage data on the one hand to design parameters on the other. Most likely this will require

a new look at parametric design, which has to be extended beyond geometric feature description in

order to allow the ﬁne-tuning of design solutions our concept rests on. What comes in here, too, is

the quality of the available data. Usage data can be rather diverse, ranging from numerical sensor

data to complex user feedback in an online forum. The overall system needs to be robust enough to

handle this diversity and the added complexity of varying information quality within a category, as

e.g., caused by sensor malfunction and an associated lack of data.

Thus our present conclusion is that though many of its elements are available today on a

sufﬁciently advanced level to realize several of its major aspects, our paradigm still bears the basic

characteristics of a vision: Some technological issues that have to be addressed we have highlighted

in the above. However, there are other, less technical challenges which need to be handled, too.

These are touching aspects like privacy, cyber security, ownership of data and product certiﬁcation,

which have to be considered on a legislative and regulatory level, too. Product certiﬁcation is a good

example in this respect, as it links technological with regulatory issues: We all rely on the safety of

the products we are using—but what if these are not accessible to physical evaluation prior to the

product’s market introduction? To answer this question, implementations of cloud-based continuous

product optimization will have to establish documented, traceable and reproducible virtual testing

procedures which make sure that even a product item that is customized to its unique user is in fact

safe to the required degree—and needless to say, the applicability of these procedures must be laid

down in the relevant regulations, meaning that they, too, need to be certiﬁed in some way.

With so many issues still open, why embark on the voyage we propose? In high-wage regions

like the USA or Europe, AM is seen as a vehicle that might help relocate production to places that

today have lost their industrial backbone to global competition: Surely, additive manufacturing in

itself has the potential to reshufﬂe global value chains. However, by presenting our concept, we believe

we have shown that to make best use of this potential, combining the power of AM, which is mainly

rooted in its ﬂexibility, with several other emerging trends in gathering, managing and analyzing

large amounts of data can lead to even more disruptive paradigm shifts in production engineering.

It is our conviction that any stakeholder willing to maintain or even strengthen his footing in the

manufacturing domain must invest in understanding both the challenges and the potentials linked to

cloud-based manufacturing in all its possible variants. With the complementary technologies that fuel

our vision currently evolving at similar, rapid pace, change may come fast, and who would want to be

the dinosaur unable to adapt in time?

32114

Sensors 2015, 15, 32079–32122

Acknowledgments:

Parts of this research received funding from the European Union’s Horizon 2020 research

and innovation programme under grant agreement no. 636951 (Manutelligence) and Grant no. 636868 (Falcon).

Parts of this research are supported by the ‘Innovative Design and Manufacturing Technologies’ project in the

Cross-Ministerial Strategic Innovation Promotion (SIP) program conducted by the Council for Science, Technology

and Innovation (CSTI), Cabinet Ofﬁce, the government of Japan. Special thanks go to Thomas Behrmann of

BIMAQ, University of Bremen, who in private discussion with the ﬁrst author laid some of the foundations of the

present work. Further, the authors would like to thank Jakub Mak-Dadanski, Behruz Khalilov and Patrick Klein

for supporting the selection of papers for the state of the art section of this paper, and Steven E. Zeltmann of New

York University for cross-checking (not only) language issues.

Conﬂicts of Interest: The authors declare no conﬂict of interest.

Abbreviations

ABM Agent-Based Manufacturing

ADT Autogenetic Design Theory

AM Additive Manufacturing

ASTM American Society for Testing and Materials

BoL Beginning of Life

CBDM Cloud-Based Design and Manufacturing

CBM Cloud-Based Manufacturing

CMfg Cloud Manufacturing

DaaS Design as a Service

DEM Discrete Element Method

DLF Directed Light Fabrication

DLP Digital Light Processing

DMD Direct Metal Deposition

DMLS Direct Metal Laser Sintering

EBAM Electron Beam Additive Manufacturing

EoL End of Life

FDM Fused Deposition Modeling

FGM Functionally Graded Materials

FEF Freeze-Form Extrusion Fabrication

HaaS Hardware-as-a-Service

IaaS Infrastructure-as-a-Service

LCE Life Cycle Engineering

LMD Laser Metal Deposition

LLM Layer-Laminated Manufacturing

LOM Laminated Object Manufacturing

MID Moulded Interconnected Device

MJM Multi-Jet Modeling

MJS Multiphase Jet Solidiﬁcation

MoL Middle of Life

MS Mask Sintering

NIST National Institute of Standards and Technology

OEM Original Equipment Manufacturer

PaaS Platform-as-a-Service

PDB Plate Diffusion Brazing

PEID Product-embedded information device

PJM Polyjet Modeling

PLM Product Life Cycle Management

32115

Sensors 2015, 15, 32079–32122

SHM Structural Health Monitoring

SaaS Software-as-a-Service

SL/SLA Stereolithography

SLM Seletive Laser Melting

SLS Selective Laser Sintering

SMD Surface Mounted Device

SMT Surface Mount Technology

STL

Stereo Lithography, alternatively Standard Tesselation Language,

a ﬁle format for representing model data for various AM systems

UAM Ultrasonic Additive Manufacturing

UC Ultrasonic Consolidation

UV ultra-violett

WBM Web-Based Manufacturing

References

Chen, D.; Heyer, S.; Ibbotson, S.; Salonitis, K.; Steingrímsson, J.G.; Thiede, S. Direct Digital Manufacturing:

Deﬁnition, Evolution, and Sustainability Implications. J. Clean. Prod. 2015, 107, 615–625. [CrossRef]

Lu, Y.; Xu, X.; Xu, J. Development of a Hybrid Manufacturing Cloud. J. Manuf. Syst.

2014

, 33, 551–566.

[CrossRef]

Xu, X. From cloud computing to cloud manufacturing. Robot. Comput.-Integr. Manuf.

2012

, 28, 75–86.

[CrossRef]

Wang, X.V.; Xu, X.W. An interoperable solution for Cloud manufacturing. Robot. Comput.-Integr. Manuf.

2013

29, 232–247. [CrossRef]

Song, T.; Liu, H.; Wei, C.; Zhang, C. Common engines of cloud manufacturing service platform for SMEs.

Int. J. Adv. Manuf. Technol. 2014, 73, 557–569. [CrossRef]

Mell, P.; Grance, T. Perspectives on Cloud Computing and Standards; National Institute of Standards and

Technology: Gaithersburg, MD, USA, 2009.

Wu, D.; Rosen, D.W.; Wang, L.; Schaefer, D. Cloud-based Manufacturing: Old Wine in New Bottles?

Procedia CIRP 2014, 17, 94–99. [CrossRef]

Wu, D.; Thames, J.L.; Rosen, D.W.; Schaefer, D. Enhancing the Product Realization Process with Cloud-Based

Design and Manufacturing Systems. J. Comput. Inf. Sci. Eng. 2013, 13, 1–14. [CrossRef]

Wu, D.; Thames, J.L.; Rosen, D.W.; Schaefer, D. Towards a Cloud-Based Design and Manufacturing Paradigm:

Looking Backward, Looking Forward; ASME: New York, NY, USA, 2012; pp. 315–328.

10.

Wu, D.; Rosen, D.W.; Wang, L.; Schaefer, D. Cloud-based design and manufacturing: A new paradigm in

digital manufacturing and design innovation. Comput.-Aided Des. 2015, 59, 1–14. [CrossRef]

11.

Shin, J.-H.; Kiritsis, D.; Xirouchakis, P. Design modiﬁcation supporting method based on product usage data

in closed-loop PLM. Int. J. Comput. Integr. Manuf. 2015, 28, 551–568. [CrossRef]

12.

Lachmayer, R.; Mozgova, I.; Sauthoff, B.; Gottwald, P. Evolutionary Approach for an Optimized Analysis of

Product Life Cycle Data. Procedia Technol. 2014, 15, 359–368. [CrossRef]

13.

Verhagen, W.J.C.; Bermell-Garcia, P.; van Dijk, R.E.C.; Curran, R. A critical review of Knowledge-Based

Engineering: An identiﬁcation of research challenges. Adv. Eng. Inform. 2012, 26, 5–15. [CrossRef]

14.

Beal, J. Engineered Self-Organization Approaches to Adaptive Design. In Proceedings of the 1st International

Conference on Through-life Engineering Services, Cranﬁeld, UK, 5–6 November 2012.

15.

Tuarob, S.; Tucker, C.S. Quantifying Product Favorability and Extracting Notable Product Features Using

Large Scale Social Media Data. J. Comput. Inf. Sci. Eng. 2015, 15, 031003–031012.

16.

Kiritsis, D. Closed-loop PLM for intelligent products in the era of the Internet of things. Comput.-Aided Des.

2011, 43, 479–501. [CrossRef]

17.

Bell, G.; Koufopanou, V. The Architecture of the Life Cycle in Small Organisms.

Philos. Trans. R. Soc. Lond. B Biol. Sci. 1991, 332, 81–89. [CrossRef]

18. Wellsandt, S.; Hribernik, K.; Thoben, K.D. Sources and characteristics of information about product use. In

Proceedings of the 25th CIRP Design Conference Innovative Product Creation, Haifa, Israel, 2–4 March 2015.

32116

Sensors 2015, 15, 32079–32122

19.

Promise Innovation. Available online: http://promise-innovation.com/components

(accessed on 17 December 2015).

20.

BOMA consortium Middle of life challenges—BOMA. Available online: http://www.holonix.it/

BOMAwordpress/middle-of-life-challenges/ (accessed on 17 December 2015).

21.

Meyer, G.; Främling, K.; Holmström, J. Intelligent Products: A survey. Comp. in Ind. April

2009

, 60, 137–148.

[CrossRef]

22.

McFarlane, D.; Sarma, S.; Chirn, J.L.; Wong, C.; Ashton, K. Auto ID systems and intelligent manufacturing

control. Eng. Appl. Artif. Intell. 2003, 16, 365–376. [CrossRef]

23.

Jun, H.-B.; Shin, J.-H.; Kiritsis, D.; Xirouchakis, P. System architecture for closed-loop PLM.

Int. J. Comput. Integr. Manuf. 2007, 20, 684–698. [CrossRef]

24.

Wuest, T.; Hribernik, K.; Thoben, K.-D. Accessing servitisation potential of PLM data by applying the product

avatar concept. Prod. Plan. Control 2015. [CrossRef]

25.

Främling, K.; Nyman, J. Information architecture for intelligent products in the internet of things.

In Proceedings of the 20th NOFOMA logistic conference, Helsinki, Finland, 5–6 June 2008.

26. Wellsandt, S.; Hribernik, K.; Thoben, K.-D. Content analysis of product usage information from embedded

sensors and web 2.0 sources. In Proceedings of the 21st ICE/IEEE International Technology Management

Conference, Belfast, Ireland, 22–24 June 2015.

27.

Oberender, C. Die Nutzungsphase und ihre Bedeutung für die Entwicklung Umweltgerechter Produkte; VDI-Verl:

Düsseldorf, Germany, 2006.

28.

Campbell, T.; Williams, C.; Ivanova, O.; Garrett, B. Could 3D Printing Change the World?

Technologies, Potential, and Implications of Additive Manufacturing is the latest Strategic

Foresight Initiative Report. Available online: http://www.atlanticcouncil.org/publications/reports/

could-3d-printing-change-the-world (accessed on 17 December 2015).

29.

ASTM standards. ASTM F2792-12a: Standard Terminology for Additive Manufacturing Technologies. West

Conshohocken, 2012. Available online: http://www.astm.org/FULL_TEXT/F2792/HTML/F2792.htm

(accessed on 17 December 2015).

30.

Michaels, S.; Sachs, E.; Cima, M. J 3-Dimensional Printing of Metal and Cermet Parts.

Adv. Powder Metall. Part. Mater. 1994.

31.

Moon, J.; Grau, J.E.; Knezevic, V.; Cima, M.J.; Sachs, E.M. Ink-Jet Printing of Binders for Ceramic Components.

J. Am. Ceram. Soc. 2004, 85, 755–762. [CrossRef]

32.

Travitzky, N.; Bonet, A.; Dermeik, B.; Fey, T.; Filbert-Demut, I.; Schlier, L.; Schlordt, T.; Greil, P.

Additive Manufacturing of Ceramic-Based Materials: Additive Manufacturing of Ceramic-Based Materials.

Adv. Eng. Mater. 2014, 16, 729–754. [CrossRef]

33.

Atwood, C.; Grifﬁth, M.; Harwell, L.; Schlinger, E.; Ensz, M.; Smugeresky, J.; Romero, J.A.; Greene, D.;

Reckaway, D. Laser Engineered Net Shaping (LENS

): A Tool for Direct Fabrication of Metal Parts. In

Proceedings of the 17th International Congress on Applications of Lasers and Elector-Optics (ICALEO’98),

Orlando, FL, USA, 16–19 November 1998; pp. E1–E7.

34.

Lewis, G.K.; Schlienger, E. Practical considerations and capabilities for laser assisted direct metal deposition.

Mater. Des. 2000, 21, 417–423. [CrossRef]

35.

Mazumder, J.; Dutta, D.; Kikuchi, N.; Ghosh, A. Closed loop direct metal deposition: Art to part.

Opt. Lasers Eng. 2000, 34, 397–414. [CrossRef]

36.

Balla, V.K.; Bose, S.; Bandyopadhyay, A. Processing of Bulk Alumina Ceramics Using Laser Engineered Net

Shaping. Int. J. Appl. Ceram. Technol. 2008, 5, 234–242. [CrossRef]

37.

Masood, S.H. Advances in Fused Deposition Modeling. In Comprehensive Materials Processing; Elsevier:

Philadelphia, PA, USA, 2014; pp. 69–91.

38.

Wu, G.; Langrana, N.A.; Sadanji, R.; Danforth, S. Solid freeform fabrication of metal components using fused

deposition of metals. Mater. Des. 2002, 23, 97–105. [CrossRef]

39.

Masood, S.H.; Song, W.Q. Development of new metal/polymer materials for rapid tooling using Fused

deposition modelling. Mater. Des. 2004, 25, 587–594. [CrossRef]

40.

Greulich, M.; Greul, M.; Pintat, T. Fast, functional prototypes via multiphase jet solidiﬁcation.

Rapid Prototyp. J. 1995, 1, 20–25. [CrossRef]

41.

Bellini, A.; Shor, L.; Guceri, S.I. New developments in fused deposition modeling of ceramics.

Rapid Prototyp. J. 2005, 11, 214–220. [CrossRef]

32117

Sensors 2015, 15, 32079–32122

42. Cesarano, J. A Review of Robocasting Technology. MRS Proc. 1998, 542. [CrossRef]

43.

Mason, M.S.; Huang, T.; Landers, R.G.; Leu, M.C.; Hilmas, G.E. Aqueous-based extrusion of high solids

loading ceramic pastes: Process modeling and control. J. Mater. Process. Technol.

2009

, 209, 2946–2957.

[CrossRef]

44.

Huang, T.; Mason, M.S.; Zhao, X.; Hilmas, G.E.; Leu, M.C. Aqueous-based freeze-form extrusion fabrication

of alumina components. Rapid Prototyp. J. 2009, 15, 88–95. [CrossRef]

45.

De Gans, B.-J.; Duineveld, P.C.; Schubert, U.S. Inkjet Printing of Polymers: State of the Art and Future

Developments. Adv. Mater. 2004, 16, 203–213. [CrossRef]

46.

Singh, R. Process capability study of polyjet printing for plastic components. J. Mech. Sci. Technol.

2011

, 25,

1011–1015. [CrossRef]

47.

Ladd, C.; So, J.-H.; Muth, J.; Dickey, M.D. 3D Printing of Free Standing Liquid Metal Microstructures.

Adv. Mater. 2013, 25, 5081–5085. [CrossRef] [PubMed]

48.

Goodridge, R.D.; Tuck, C.J.; Hague, R.J.M. Laser sintering of polyamides and other polymers.

Prog. Mater. Sci.

2012, 57, 229–267. [CrossRef]

49.

Kruth, J.P.; Froyen, L.; van Vaerenbergh, J.; Mercelis, P.; Rombouts, M.; Lauwers, B. Selective laser melting of

iron-based powder. J. Mater. Process. Technol. 2004, 149, 616–622. [CrossRef]

50.

Bourell, D.L.; Marcus, H.L.; Barlow, J.W.; Beaman, J.J. Selective laser sintering of metals and ceramics.

Int. J. Powder Metall. 1992, 28, 369–381.

51.

Murr, L.E.; Martinez, E.; Amato, K.N.; Gaytan, S.M.; Hernandez, J.; Ramirez, D.A.; Shindo, P.W.; Medina, F.;

Wicker, R.B. Fabrication of Metal and Alloy Components by Additive Manufacturing: Examples of 3D

Materials Science. J. Mater. Res. Technol. 2012, 1, 42–54. [CrossRef]

52.

Gong, X.; Anderson, T.; Chou, K. Review on Powder-Based Electron Beam Additive Manufacturing Technology;

ASME: New York, NY, USA, 2012.

53. Qian, B.; Shen, Z. Laser sintering of ceramics. J. Asian Ceram. Soc. 2013, 1, 315–321. [CrossRef]

54.

Mueller, B.; Kochan, D. Laminated object manufacturing for rapid tooling and patternmaking in foundry

industry. Comput. Ind. 1999, 39, 47–53. [CrossRef]

55.

Park, J.; Tari, M.J.; Hahn, H.T. Characterization of the laminated object manufacturing (LOM) process.

Rapid Prototyp. J. 2000, 6, 36–50. [CrossRef]

56.

Chiu, Y.Y.; Liao, Y.S.; Hou, C.C. Automatic fabrication for bridged laminated object manufacturing (LOM)

process. J. Mater. Process. Technol. 2003, 140, 179–184. [CrossRef]

57.

Yi, S.; Liu, F.; Zhang, J.; Xiong, S. Study of the key technologies of LOM for functional metal parts.

J. Mater. Process. Technol. 2004, 150, 175–181. [CrossRef]

58.

Strasser, C.; Prihodovsky, A.; Ploshikhin, V. Plate press brazing for the production of large metallic

tools. In Proceedings of the 1st International Symposium Materials Science and Technology of Additive

Manufacturing, Bremen, Germany, 27–28 May 2014.

59.

Hull, C.W. Apparatus for Production of Three-Dimensional Objects by Stereolithography. U.S. Patent

4,575,330, 11 March 1986.

60.

Lee, J.W.; Lee, I.H.; Cho, D.-W. Development of micro-stereolithography technology using metal powder.

Microelectron. Eng. 2006, 83, 1253–1256. [CrossRef]

61.

Bartolo, P.J.; Gaspar, J. Metal ﬁlled resin for stereolithography metal part. CIRP Ann.-Manuf. Technol.

2008

57, 235–238. [CrossRef]

62.

Brady, G.A.; Chu, T.M. Curing Behavior of Ceramic Resin for Stereolithography. In Proceedings of the Solid

Freeform Fabrication Proceedings, Austin, TX, USA, 12–14 August 1996.

63.

Doreau, F.; Chaput, C.; Chartier, T. Stereolithography for Manufacturing Ceramic Parts. Adv. Eng. Mater.

2000, 2, 493–496. [CrossRef]

64.

Chartier, T.; Chaput, C.; Doreau, F.; Loiseau, M. Stereolithography of structural complex ceramic parts.

J. Mater. Sci. 2002, 37, 3141–3147. [CrossRef]

65.

Jorge Lino, F.; Vasconcelos, P. V.; Neto, R. J. L.; Paiva, R. Stereolithography, the Front Edge of Rapid

Prototyping. Materials Science Forum 2008, 587–588, 998–1002. [CrossRef]

66.

Conner, B.P.; Manogharan, G.P.; Martof, A.N.; Rodomsky, L.M.; Rodomsky, C.M.; Jordan, D.C.; Limperos, J.W.

Making sense of 3-D printing: Creating a map of additive manufacturing products and services. Addit.

Manuf. 2014, 1–4, 64–76. [CrossRef]

32118

Sensors 2015, 15, 32079–32122

67.

Macdonald, E.; Salas, R.; Espalin, D.; Perez, M.; Aguilera, E.; Muse, D.; Wicker, R.B. 3D Printing for the Rapid

Prototyping of Structural Electronics. IEEE Access 2014, 2, 234–242. [CrossRef]

68.

Khajavi, S.H.; Partanen, J.; Holmström, J. Additive manufacturing in the spare parts supply chain.

Comput. Ind. 2014, 65, 50–63. [CrossRef]

69. Vujovi´c, I. The Introduction of 3D Printing into the Maritime Industry. Trans. Marit. Sci. 2015, 04, 86–87.

70.

Isaza, P.J.F.; Aumund-Kopp, C. Additive Manufacturing Approaches. In Structural Materials and Processes in

Transportation; Lehmhus, D., Busse, M., Herrmann, A.S., Kayvantash, K., Eds.; Wiley-VCH Verlag GmbH &

Co. KGaA: Weinheim, Germany, 2013; pp. 549–566.

71.

Lang, W.; Jakobs, F.; Tolstosheeva, E.; Sturm, H.; Ibragimov, A.; Kesel, A.; Lehmhus, D.; Dicke, U. From

embedded sensors to sensorial materials—The road to function scale integration. Sens. Actuators Phys.

2011

171, 3–11. [CrossRef]

72.

Dumstorff, G.; Paul, S.; Lang, W. Integration Without Disruption: The Basic Challenge of Sensor Integration.

IEEE Sens. J. 2014, 14, 2102–2111. [CrossRef]

73. Chortos, A.; Bao, Z. Skin-inspired electronic devices. Mater. Today 2014, 17, 321–331. [CrossRef]

74.

Seminara, L.; Pinna, L.; Ibrahim, A.; Noli, L.; Caviglia, S.; Gastaldo, P. Towards integrating intelligence in

electronic skin. Mechatronics 2015. in press. [CrossRef]

75.

McEvoy, M.A.; Correll, N. Materials that couple sensing, actuation, computation, and communication.

Science 2015, 347, 1261689. [CrossRef] [PubMed]

76.

Parteli, E.J.R. DEM simulation of particles of complex shapes using the multisphere method: Application for

additive manufacturing. Friedrich-Alexander-Univ. Erlangen-Nürnberg FAU 2013, 1542, 185–188.

77.

Kumar, S.; Kruth, J.-P. Composites by rapid prototyping technology. Mater. Des.

2010

, 31, 850–856. [CrossRef]

78.

Vaezi, M.; Chianrabutra, S.; Mellor, B.; Yang, S. Multiple material additive manufacturing.

Virtual Phys. Prototyp. 2013, 8, 19–50. [CrossRef]

79.

Guo, N.; Leu, M.C. Additive manufacturing: Technology, applications and research needs.

Front. Mech. Eng.

2013, 8, 215–243. [CrossRef]

80.

Weiss, L.E.; Merz, R.; Prinz, F.B.; Neplotnik, G.; Padmanabhan, P.; Schultz, L.; Ramaswami, K. Shape

deposition manufacturing of heterogeneous structures. J. Manuf. Syst. 1997, 16, 239–248. [CrossRef]

81.

Godlinski, D.; Morvan, S. Steel Parts with Tailored Material Gradients by 3D-Printing Using Nano-Particulate

Ink. In Materials Science Forum; Trans Tech Publications Ltd.: Uetikon-Zürich, Switzerland, 2005.

82.

Maiwald, M.; Werner, C.; Zöllmer, V.; Busse, M. INKtelligent printing

for sensorial applications.

Sens. Rev.

2010, 30, 19–23. [CrossRef]

83.

Zöllmer, V.; Pál, E.; Maiwald, M.; Werner, C.; Godlinski, D.; Lehmhus, D. Functional materials for printed

sensor structures. In Proceedings of the 1st Joint International Symposium on System-Integrated Intelligence

2012, Hannover, Germany, 22–29 June 2012.

84.

Hoerber, J.; Glasschroeder, J.; Pfeffer, M.; Schilp, J.; Zaeh, M.; Franke, J. Approaches for Additive

Manufacturing of 3D Electronic Applications. Procedia CIRP 2014, 17, 806–811. [CrossRef]

85.

Paulsen, J.A.; Renn, M.; Christenson, K.; Plourde, R. Printing conformal electronics on 3D structures with

Aerosol Jet technology. In Proceedings of the 2012 Future of Instrumentation International Workshop (FIIW),

Gatlinburg, TN, USA, 8–9 October 2012.

86.

Shemelya, C.; Cedillos, F.; Aguilera, E.; Espalin, D.; Muse, D.; Wicker, R.; MacDonald, E. Encapsulated

Copper Wire and Copper Mesh Capacitive Sensing for 3-D Printing Applications. IEEE Sens. J.

2015

, 15,

1280–1286. [CrossRef]

87.

Li, J.; Monaghan, T.; Masurtschak, S.; Bournias-Varotsis, A.; Friel, R.J.; Harris, R.A. Exploring the mechanical

strength of additively manufactured metal structures with embedded electrical materials. Mater. Sci. Eng. A

2015, 639, 474–481. [CrossRef]

88.

Joe Lopes, A.; MacDonald, E.; Wicker, R.B. Integrating stereolithography and direct print technologies for

3D structural electronics fabrication. Rapid Prototyp. J. 2012, 18, 129–143. [CrossRef]

89.

Johander, P.; Haasl, S.; Persson, K.; Harrysson, U. Layer Manufacturing as a Generic Tool for Microsystem

Integration. In Proceedings of the Third International Conference on Multi-Material Micro Manufacture,

Borovets, Bulgaria, 3–5 October 2007.

90.

Voxel8—3D Printed Electronics. Available online: http://www.voxel8.co/ (accessed on 17 December 2015).

91.

Rachel, G. Trends in Commercial 3D Printing and Additive Manufacturing. 3D Print. Addit. Manuf.

2015

, 2,

89–90.

32119

Sensors 2015, 15, 32079–32122

92.

Espalin, D.; Muse, D.W.; MacDonald, E.; Wicker, R.B. 3D Printing multifunctionality: Structures with

electronics. Int. J. Adv. Manuf. Technol. 2014, 72, 963–978. [CrossRef]

93.

Klein, P.; Luetzenberger, J.; Thoben, K.-D. A proposal for knowledge formalization in product development

processes. In Proceedings of the 20th International Conference on Engineering Design (ICED 15), Milan,

Italy, 27–30 July 2015.

94.

Denkena, B.; Mörke, T.; Krüger, M.; Schmidt, J.; Boujnah, H.; Meyer, J.; Gottwald, P.; Spitschan, B.;

Winkens, M. Development and ﬁrst applications of gentelligent components over their lifecycle.

CIRP J. Manuf. Sci. Technol. 2014, 7, 139–150. [CrossRef]

95.

Lachmayer, R.; Mozgova, I.; Sauthoff, B.; Gottwald, P. Product Evolution and Optimization Based on

Gentelligent Components and Product Life Cycle Data. In Smart Product Engineering; Lecture Notes in

Production Engineering; Abramovici, M., Stark, R., Eds.; Springer Berlin Heidelberg: Berlin, Heidelberg,

2013; pp. 685–694.

96.

Hehenberger, P. An Approach to Model-based Parametric Design of Mechatronic Systems.

Comput.-Aided Des. Appl. 2015, 12, 282–289. [CrossRef]

97. Pfeifer, T. Qualitätsmanagement: Strategien, Methoden, Techniken; Hanser: Munich, Germany, 2001.

98.

Fathi, M.; Holland, A. Knowledge-based feedback integration to facilitate sustainable product innovation. In

Proceedings of the IEEE Conference on Emerging Technologies & Factory Automation, 2009 (ETFA 2009),

Mallorca. Spain, 22–25 Septemer 2009; pp. 1–8.

99.

Espíndola, D.B.; Fumagalli, L.; Garetti, M.; Pereira, C.E.; Botelho, S.S.C.; Ventura Henriques, R. A model-based

approach for data integration to improve maintenance management by mixed reality. Comput. Ind.

2013

, 64,

376–391. [CrossRef]

100.

Abramovici, M.; Lindner, A. Providing product use knowledge for the design of improved product

generations. CIRP Ann.-Manuf. Technol. 2011, 60, 211–214. [CrossRef]

101.

Abramovici, M.; Lindner, A.; Walde, F.; Fathi, M.; Dienst, S. Decision Support for Improving the Design of

Hydraulic Systems by Leading Feedback into Product development. In Impacting Society through Engineering

Design; Culley, S.J., Ed.; The Design Society: Copenhagen, Denmark, 2011; pp. 1–10.

102.

Ajayi, M.; Smart, P. Innovation and learning: Exploring feedback from service to design.

Proc. Inst. Mech. Eng. Part B J. Eng. Manuf. 2008, 222, 1195–1199. [CrossRef]

103.

Bleda, A.L.; Maestre, R.; Santa, G.; Jara, A.J.; Skarmeta, A.G. Web of Things as a Product Improvement tool:

Furniture as Case Study. In Proceedings of the 2012 Sixth International Conference on Innovative Mobile

and Internet Services in Ubiquitous Computing (IMIS), Palermo, Italy, 4–6 July 2012.

104.

Bueno, M.R.; Milton, B. A Method for Identifying Product Improvement Opportunities through Warranty

Data. Adv. Transdiscipl. Eng. 2014, 1, 122–131.

105.

D’Antonio, G.; Sauza Bedolla, J.; Chiabert, P.; Lombardi, F. PLM-MES Integration to Support Collaborative

Design. In Proceedings of the 20th International Conference on Engineering Design (ICED 15), Milan, Italy,

15 July 2015.

106.

Dornhöfer, M.; Fathi, M.; Holland, A. Applying Rules for Representing and Reasoning of Objective

Product Use Information Exemplarily for Injection Molding Machines. In Informatics Engineering and

Information Science; Communications in Computer and Information Science; Springer Berlin Heidelberg:

Berlin, Heidelberg, 2011; pp. 616–623.

107.

Johansson, C.-A.; Simon, V.; Galar, D. Context Driven Remaining Useful Life Estimation. Procedia CIRP

2014

22, 181–185. [CrossRef]

108.

Røstad, C.C.; Henriksen, B. ECO-Boat MOL Capturing Data from Real Use of the Product. In Product

Lifecycle Management. Towards Knowledge-Rich Enterprises; IFIP Advances in Information and Communication

Technology; Rivest, L., Bouras, A., Louhichi, B., Eds.; Springer Berlin Heidelberg: Berlin, Germany, 2012;

pp. 99–110.

109.

Melchels, F.P.W.; Feijen, J.; Grijpma, D.W. A review on stereolithography and its applications in biomedical

engineering. Biomaterials 2010, 31, 6121–6130. [CrossRef] [PubMed]

110.

Murr, L.E.; Gaytan, S.M.; Medina, F.; Lopez, H.; Martinez, E.; Machado, B.I.; Hernandez, D.H.; Martinez, L.;

Lopez, M.I.; Wicker, R.B.; et al. Next-generation biomedical implants using additive manufacturing of

complex, cellular and functional mesh arrays. Philos. Trans. R. Soc. Math. Phys. Eng. Sci.

2010

, 368, 1999–2032.

[CrossRef] [PubMed]

111. Van Noort, R. The future of dental devices is digital. Dent. Mater. 2012, 28, 3–12. [CrossRef] [PubMed]

32120

Sensors 2015, 15, 32079–32122

112.

Kandala, N.-B.; Connock, M.; Pulikottil-Jacob, R.; Sutcliffe, P.; Crowther, M.J.; Grove, A.; Mistry, H.; Clarke, A.

Setting benchmark revision rates for total hip replacement: Analysis of registry evidence. BMJ

2015

, 350,

h756. [CrossRef] [PubMed]

113.

Ledet, E.H.; D’Lima, D.; Westerhoff, P.; Szivek, J.A.; Wachs, R.A.; Bergmann, G. Implantable Sensor

Technology: From Research to Clinical Practice. J. Am. Acad. Orthop. Surg.

2012

, 20, 383–392. [CrossRef]

[PubMed]

114.

Bergmann, G.; Graichen, F.; Siraky, J.; Jendrzynski, H.; Rohlmann, A. Multichannel strain gauge telemetry

for orthopaedic implants. J. Biomech. 1988, 21, 169–176. [CrossRef]

115.

Damm, P.; Graichen, F.; Rohlmann, A.; Bender, A.; Bergmann, G. Total hip joint prosthesis for

in vivo

measurement of forces and moments. Med. Eng. Phys. 2010, 32, 95–100. [CrossRef] [PubMed]

116.

Graichen, F.; Arnold, R.; Rohlmann, A.; Bergmann, G. Implantable 9-channel telemetry system for

in vivo

load measurements with orthopedic implants. IEEE Trans. Biomed. Eng.

2007

, 54, 253–261. [CrossRef]

[PubMed]

117.

Bergmann, G.; Graichen, F.; Rohlmann, A.; Westerhoff, P.; Heinlein, B.; Bender, A.; Ehrig, R. Design and

Calibration of Load Sensing Orthopaedic Implants. J. Biomech. Eng.

2008

, 130, 021009. [CrossRef] [PubMed]

118.

Campoli, G.; Weinans, H.; van der Helm, F.; Zadpoor, A.A. Subject-speciﬁc modeling of the scapula bone

tissue adaptation. J. Biomech. 2013, 46, 2434–2441. [CrossRef] [PubMed]

119.

Campoli, G.; Bolsterlee, B.; van der Helm, F.; Weinans, H.; Zadpoor, A.A. Effects of densitometry, material

mapping and load estimation uncertainties on the accuracy of patient-speciﬁc ﬁnite-element models of the

scapula. J. R. Soc. Interface R. Soc. 2014, 11, 20131146. [CrossRef] [PubMed]

120.

Zadpoor, A.A.; Weinans, H. Patient-speciﬁc bone modeling and analysis: The role of integration and

automation in clinical adoption. J. Biomech. 2015, 48, 750–760. [CrossRef] [PubMed]

121.

Cronskär, M.; Bäckström, M.; Rännar, L. Production of customized hip stem prostheses—A comparison

between conventional machining and electron beam melting (EBM). Rapid Prototyp. J.

2013

, 19, 365–372.

[CrossRef]

122.

Burblies, A.; Busse, M. Computer based porosity design by multi phase topology optimization. In Multiscale

and Functionally Graded Materials, Proceedings of the AIP Conference on International Conference, Oahu

Island, Hawaii, 15–18 October 2006.

123. Gershenson, C. Design and Control of Self-Organizing Systems; CopIt Arxives: Mexico City, Mexico, 2007.

124.

Ferreira, J.D.; Ribeiro, L.; Onori, M.; Barata, J. Bio-inspired Self-Organising Methodologies for Production

Emergence. In Proceedings of the 2013 IEEE International Conference on Systems, Man, and Cybernetics

(SMC), Manchester, UK, 13–16 October 2013.

125.

Caridi, M.; Sianesi, A. Multi-agent systems in production planning and control: An application to the

scheduling of mixed-model assembly lines. Int. J. Prod. Econ. 2000, 68, 29–42. [CrossRef]

126.

Leitao, P. Industrial Agents: Emerging Applications of Software Agents in Industry, 1st ed.; Elsevier: Waltham,

MA, USA, 2015.

127.

Marik, V.; McFarlane, D. Industrial Adoption of Agent-Based Technologies. IEEE Intell. Syst.

2005

, 20, 27–35.

[CrossRef]

128.

Pˇechouˇcek, M.; Maˇrík, V. Industrial deployment of multi-agent technologies: Review and selected case

studies. Auton. Agents Multi-Agent Syst. 2008, 17, 397–431. [CrossRef]

129.

Bosse, S. Design and Simulation of Material-Integrated Distributed Sensor Processing with a Code-Based

Agent Platform and Mobile Multi-Agent Systems. Sensors 2015, 15, 4513–4549. [CrossRef] [PubMed]

130.

Bosse, S. Uniﬁed Distributed Computing and Co-ordination in Pervasive/Ubiquitous Networks with Mobile

Multi-Agent Systems using a Modular and Portable Agent Code Processing Platform.

Procedia Comput. Sci.

2015, 63, 56–64. [CrossRef]

131. Bosse, S.; Lechleiter, A. Structural Health and Load Monitoring with Material-embedded Sensor Networks

and Self-organizing Multi-agent Systems. Procedia Technol. 2014, 15, 668–690. [CrossRef]

132.

Fujii, N.; Qian, Y.; Kaihara, T. Real Virtual Fusion Production Scheduling Using Social Contract-based

Approach Effectiveness of Adjusting Virtual System Size. Procedia CIRP 2012, 3, 525–530. [CrossRef]

133.

Lehmhus, D.; Brugger, J.; Muralt, P.; Pane, S.; Ergeneman, O.; Dubois, M.-A.; Gupta, N.; Busse, M. When

nothing is constant but change: Adaptive and sensorial materials and their impact on product design.

J. Intell. Mater. Syst. Struct. 2013, 24, 2172–2182. [CrossRef]

32121

Sensors 2015, 15, 32079–32122

134.

Myung, S.; Han, S. Knowledge-based parametric design of mechanical products based on conﬁguration

design method. Expert Syst. Appl. 2001, 21, 99–107. [CrossRef]

135.

Zhang, W.-H.; Beckers, P.; Fleury, C. A uniﬁed parametric design approach to structural shape optimization.

Int. J. Numer. Methods Eng. 1995, 38, 2283–2292. [CrossRef]

136.

Shahpar, S.; Lapworth, L. PADRAM: Parametric Design and Rapid Meshing System for Turbomachinery

Optimisation. In Proceedings of the 2003 International Joint Power Generation Conference, Atlanta, GA,

USA, 16–19 June 2003.

137.

Yanagawa, K. Conﬂuence of Parametric Design and Digital Fabrication Restructuring Manufacturing

Industries. In Proceedings of the 20th International Conference of the Association for Computer-Aided

Architectural Design Research in Asia, Daegu, Korea, 20–22 May 2015.

138. Woodbury, R. Elements of Parametric Design; Taylor and Francis: New York, NY, USA, 2010.

139.

Gershenfeld, N.; Carney, M.; Jenett, B.; Calisch, S.; Wilson, S. Macrofabrication with Digital Materials: Robotic

Assembly. Archit. Des. 2015, 85, 122–127. [CrossRef]

140.

Doubrovski, E.L.; Tsai, E.Y.; Dikovsky, D.; Geraedts, J.M.P.; Herr, H.; Oxman, N. Voxel-based fabrication

through material property mapping: A design method for bitmap printing. Comput.-Aided Des.

2015

, 60,

3–13. [CrossRef]

141.

Bass, L.B.; Meisel, M.A.; Williams, C.B. Exploring Variability in Material Properties of Multi-Material Jetting

Parts. In Proceedings of the 26th Annual International Solid Freeform Fabrication (SFF) Symposium—An

Additive Manufacturing Conference, Austin, Texas, USA, 10-12 August 2015; pp. 993–1006.

142.

Davenport, T.H.; Patil, D.J. Data Scientist: The Sexiest Job of the 21st Century. Available online: https://

hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century (accessed on 17 December 2015).

143.

Zhu, H.; Madnick, S.; Lee, Y.; Wang, R. Data and Information Quality Research: Its Evolution and Future; Working

Paper; MIT: Cambridge, MA, USA, 2012.

144.

Li, C.; Wang, S.; Kang, L.; Guo, L.; Cao, Y. Trust evaluation model of cloud manufacturing service platform.

Int. J. Adv. Manuf. Technol. 2014, 75, 489–501. [CrossRef]

145.

Lu, Y.; Xu, X. Protecting Intellectual Property in a Cloud Manufacturing Environment: Requirements

and Strategies. In Advances in Production Management Systems: Innovative Production Management towards

Sustainable Growth; IFIP Advances in Information and Communication Technology; Umeda, S., Nakano, M.,

Mizuyama, H., Hibino, H., Kiritsis, D., von Cieminski, G., Eds.; Springer International Publishing: Cham,

Switzerland, 2015; pp. 404–411.

146.

Maropoulos, P.G.; Ceglarek, D. Design veriﬁcation and validation in product lifecycle.

CIRP Ann.-Manuf. Technol. 2010, 59, 740–759. [CrossRef]

147.

Lachmayer, R.; Mozgova, I.; Reimche, W.; Colditz, F.; Mroz, G.; Gottwald, P. Technical Inheritance: A Concept

to Adapt the Evolution of Nature to Product Engineering. Procedia Technol. 2014, 15, 178–187. [CrossRef]

148.

Vajna, S.; Clement, S.; Jordan, A.; Bercsey, T. The Autogenetic Design Theory: An evolutionary view of the

design process. J. Eng. Des. 2005, 16, 423–440. [CrossRef]

2015 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access

article distributed under the terms and conditions of the Creative Commons by Attribution

(CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

32122