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Procedia
CIRP 93
(2020) 389–394
www.elsevier.com/locate/procedia
53rd
Systems
53rdCIRP
CIRPConference
Conference on
on Manufacturing
Manufacturing Systems
28th CIRP Design
Conference,
May 2018,Platform
Nantes, France
A Genetic Algorithm-Based
Model
for Product
Design for Hybrid
Manufacturing
A new methodology to analyze the functional and physical architecture of
a
a
Moussaoriented
, Hoda ElMaraghy
* family identification
existing products for anMostafa
assembly
product
a
Industrial and Manufacturing Systems Engineering, University of Windsor, 401 Sunset Ave. Windsor, Ontario N9B 3P4, Canada
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. E-mail address: hae@uwindsor.ca
École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France
Abstract
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: paul.stief@ensam.eu
Nowadays, manufacturers aim at satisfying diverse and changing customer demand in order to survive in the competitive market. An approach
that combines several manufacturing concepts, including product platform formation and hybrid manufacturing, is proposed in order to effectively
manage the product variety. A genetic algorithm-based model is introduced to design the optimal or near-optimal platform for large sets of
Abstract
products and features that can be further manufactured by additive and/or subtractive manufacturing to be customized into different product
variants. An illustrative example is used to demonstrate the model. The proposed model leads to better management of product proliferation.
In today’s business environment, the trend towards more product variety and customization is unbroken. Due to this development, the need of
agile ©
and
reconfigurable
emerged
2019
The
Published
Elsevier
B.V.
©
2020
TheAuthors.
Authors.production
Publishedbysystems
by
Elsevier
B.V. to cope with various products and product families. To design and optimize production
systems
as
well
as
to
choose
the
optimal
product
matches,
product
analysis methods are needed. Indeed, most of the known methods aim to
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
This is an open access article under the CC BY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
analyze
a
product
or
one
product
family
on
the
physical
level.
Different
families,
however,
may differ
largely
in terms of the number and
Peer-review
under
responsibility
of
the
scientific
committee
of
the
53rd
CIRPCIRP
Conference
on Manufacturing
Systems
Peer-review under responsibility of the scientific committee of the product
53rd
Conference
on Manufacturing
Systems
nature of components. This fact impedes an efficient comparison and choice of appropriate product family combinations for the production
Keywords:
Platfrom;isDelayed
Product
Differentiation;
Hybrid
Manufacturing;
system.
A new Product
methodology
proposed
to analyze
existing
products
in view Additive
of their Manufacturing
functional and physical architecture. The aim is to cluster
these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable
assembly systems. Based on Datum Flow Chain, the physical structure of the products is analyzed. Functional subassemblies are identified, and
a functional analysis is performed. Moreover, a hybrid functional and physical architecture graph (HyFPAG) is the output which depicts the
1. Introduction
printed part quality, increase in the range and types of printable
similarity between product families by providing design support to both, production system planners and product designers. An illustrative
materials. Thus, the shift from traditional manufacturing to
example of a nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of
Nowadays, there is an increasing pressure from the market
hybrid manufacturing, in which the additive manufacturing is
thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach.
on
manufacturers
to
offer
variety.
This
pressure
is
a
result
of
combined with subtractive manufacturing, enabled by Industry
© 2017 The Authors. Published by Elsevier B.V.
the
globalization
in
which
different
product
requirements
are
4.0 Design
would Conference
fundamentally
Peer-review under responsibility of the scientific committee of the 28th CIRP
2018.alter the way the part/ product is
needed due to the differences in geography, culture,
realized and helps in handling manufacturing challenges in
government
environmental
regulations.
more efficient way [7]. Hybrid manufacturing can defined as
Keywords:
Assembly;legislations
Design method;and
Family
identification
Moreover, even within the local markets, there is a pressure
the combination of two or more manufacturing operations, each
since customers require different designs, functionalities, etc.
of which is from different manufacturing technologies and has
Thus, the manufacturers have to offer multiple variants within
interactions with and influences on each other [8]. Combining
the product family to fulfill different market needs [1, 2].
additive
and subtractive
among the most
1. Introduction
of
the product
range andmanufacturing
characteristicsis manufactured
and/or
Product variety is a double edge weapon. The wide variety in
commonly used
hybrid
manufacturing.
assembled
in this
system.
In this context, the main challenge in
the offered
a competitive
This paper
at is
utilizing
additive
subtractive
Due
to theproducts
fast provides
development
in theadvantage
domainto the
of
modelling
and aims
analysis
now not
only toand
cope
with single
manufacturers
since
customers
can
choose
products
that
fulfill
manufacturing
in
order
to
deal
with
the
product
proliferation
communication and an ongoing trend of digitization and
products, a limited product range or existing product families,
their specific
needs. However,
on theareother
side,
it adds
challenges.
be achieved
designing
a product
digitalization,
manufacturing
enterprises
facing
important
but
also to beThis
ablecan
to analyze
and toby
compare
products
to define
complexity to designing, planning and manufacturing of
platform that includes common (core) features and can be
challenges in today’s market environments: a continuing
new product families. It can be observed that classical existing
products and increases the managerial burdens [3, 4].
customized by additive and subtractive manufacturing based on
tendency towards reduction of product development times and
product families are regrouped in function of clients or features.
3D printing moves beyond the realm of prototyping to
the customers’ demand. Product platform is defined as a set of
shortened
product lifecycles.
addition,
there is [5,
an increasing
However,
assembly
oriented
product
arethat
hardly
to afind.
manufacturing
functional In
parts
and products
6]. This is
sub-systems
(i.e. features
or parts)
andfamilies
interfaces
form
demand
of
customization,
being
at
the
same
time
in
a
global
On
the
product
family
level,
products
differ
mainly
in two
enabled by the exponential advances in additive manufacturing
common structure from which a stream of derivative products
competition
with
competitors
all
over
the
world.
This
trend,
main
characteristics:
(i)
the
number
of
components
and
(ii)
the
in terms of faster processing, significant enhancement in the
can be efficiently produced and developed [9].
which is inducing the development from macro to micro
type of components (e.g. mechanical, electrical, electronical).
markets,
results
inThe
diminished
lot sizes
dueB.V.
to augmenting
Classical methodologies considering mainly single products
2212-8271
© 2019
Authors. Published
by Elsevier
This varieties
is an open access
article under the
BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/)
product
(high-volume
to CC
low-volume
production)
[1].
or solitary, already existing product families analyze the
Peer-review
responsibility of variety
the scientific
CIRP
on Manufacturing
To cope
with under
this augmenting
as committee
well as of
tothe
be53rd
able
to Conference
product
structure onSystems
a physical level (components level) which
identify possible optimization potentials in the existing
causes difficulties regarding an efficient definition and
production system, it is important to have a precise knowledge
comparison of different product families. Addressing this
2212-8271 © 2020 The Authors. Published by Elsevier B.V.
This is an©open
article Published
under theby
CC
BY-NC-ND
2212-8271
2017access
The Authors.
Elsevier
B.V. license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review
under
responsibility
of scientific
the scientific
committee
theCIRP
53rdDesign
CIRP Conference
Conference2018.
on Manufacturing Systems
Peer-review
under
responsibility
of the
committee
of the of
28th
10.1016/j.procir.2020.04.044
390
2
Mostafa Moussa et al. / Procedia CIRP 93 (2020) 389–394
Author name / Procedia CIRP 00 (2019) 000–000
A product platform is a cornerstone in delaying the product
differentiation, which is one of the effective strategies to handle
the problems arise from the product proliferation. The
manufacturers can benefit from mass-producing of the product
platform in terms of lower labour costs, faster rate of
production and efficiently utilizes resources. Moreover, they
benefit from the ability of hybrid manufacturing to customize
the product platform to satisfy the different customers’
demands. This is known as delayed product differentiation.
A genetic algorithm (GA)-based model that is able to design
a product platform for hybrid manufacturing is proposed. The
model objective is to design the additive/subtractive product
platform while minimizing the total manufacturing cost of the
customer demand.
The rest of the paper is organized as follows. The review of
the existing research on the product platform design is
discussed in Section 2. The additive/subtractive product
platform is described in Section 3. Section 4 discusses the
proposed GA-based model for the additive/subtractive product
platform design. Section 5 presents an illustrative example. The
summary and conclusions are outlined in Section 6.
2. Literature review
During the last decades, the product platform design has
gained much attention from the industry and academia. Thus,
there has been a significant amount of research conducted in
that area. This was the reason that derived many authors to
publish literature review papers that gives an overview of key
findings, concepts and developments in relation to the product
platform such as Simpson [10], Jose and Tollenaere [11], Jiao,
Simpson, and Siddique [12], Zhang [13], Facinet al. [14] and
Han et al. [15].
Moon et al. [16] proposed a dynamic multi-agent system
based on negotiation mechanisms to design a platform. The
developed model was applied to design a platform for a family
of power tools. The model is limited to small size problems.
Olivares-Benitez and Gonzalez-Velarde [17] developed a
metaheuristic approach based on Scatter Search and Tabu
Search to find a common platform for a modular product. The
selection of a common platform was based on the product
performance and manufacturing cost. Ben-Arieh et al. [18]
proposed a mathematical model and a GA model to determine
multiple platforms’ configuration for the production of a given
product family while minimizing the overall production cost.
The product variants are produced by assembling and/or
disassembling parts from the platforms. The number of the
platforms were specified a priori. A family of cordless drills
was used for illustration.
AlGeddawy and ElMaraghy [19] proposed a reactive design
methodology for the product platform. The methodology was
based on physical commonality rather than the commonality
index. Cladistics was used to design a core platform by
hierarchically clustering common components as well as
combine the common parts into integral parts and modules, if
possible. The model was capable of balancing between the
Design for Manufacturing and Assembly (DFMA) and product
modularity. The proposed model was applied to household
kettles family. Hanafy and ElMaraghy [20] developed a
methodology using phylogenetic networks for forming product
platforms and determining the assembly line layout of modular
product families. The concept of assembly/disassembly
modular platforms was utilized. For the proposed model
demonstration, a family of household kettles was used.
ElMaraghy and Abbas [21] introduced a new methodology
called Co-platforming or mapping product features platform
and corresponding manufacturing system machines platform.
Zheng et al. [22] presented a conceptual framework of a
personalized product configuration system based on the
adaptable open architecture product platform. For validation,
the framework was applied to an illustrative example of a
personalized bicycle configuration. Zhang et al. [23] proposed
a method for product platform planning using the existing
product data in the product lifecycle management (PLM)
database. To show the effectiveness of the proposed method, it
was applied using the product data in the PLM database of a
valve company. Kim and Moon [24] presented a methodology
to identify a sustainable platform by integrating sustainability
values, risk values, and commonality.
Galizia et al. [25] proposed a decision support system for
product platforms design and selection in high variety
manufacturing. The proposed decision support system was
applied to a case study for a family of valves. ElMaraghy and
Moussa [26] introduced the notion of utilizing additive and
subtractive manufacturing in designing a product platform. A
mathematical programming model was proposed for that
purpose. A case study for a product family of the guiding
bushes was used for illustration.
The majority of the literature in the area of the product
platform focus on the assembly domain. Only ElMaraghy and
Moussa [26] considered additive/subtractive product platform
design. The critical limitation of their model is that the
proposed model has limited computational capacity. In other
words, it can design a product platform for a product family
with a limited number of product variants and features. This is
what the proposed work, based on utilizing a meta-heuristic
algorithm, is designed to overcome.
3. Product Platform for Hybrid Manufacturing
Manufacturers strive to increase their competitiveness and
responsiveness. Adopting the exponential technological
advances such as additive manufacturing can boost the
manufacturers’ competitiveness. In this paper, an
additive/subtractive product platform concept is introduced.
The product platform can be customized by adding features
through additive manufacturing and/or removing features
through subtractive manufacturing to obtain different product
variants that fall within a considered product family. This
product platform plays a vital role in applying the delayed
product differentiation strategy in which the manufacturers
benefit from mass-producing the product platform and the
ability of its customization.
Fig. 1 shows an example to illustrate the proposed product
platform for hybrid manufacturing. The product platform in the
figure is for a family consists of three product variants. This
product platform is formed of features F1, F2, F3 and F4. In
order to obtain product variant 1, features F2 and F4 are
Mostafa Moussa et al. / Procedia CIRP 93 (2020) 389–394
Author name / Procedia CIRP 00 (2019) 000–000
removed from the product platform by subtractive
manufacturing, and feature F5 is added to the product platform
by additive manufacturing. Product variant 2 is produced using
subtractive manufacturing to remove feature F4 from the
product platform. Finally, subtractive manufacturing is used to
remove features F3 and F4 from the product platform, and
additive manufacturing is used to add feature F6 to the product
platform to have product variant 3.
3
The problem considered in this work can be described as:
for a product family which consists of K product variants and
a total of J features, a product platform that is made of a set of
features can be formed and further transformed with minimum
cost into product variants of the considered family.
4. Genetic Algorithm-based model for Product Platform
Design
The increase of the number of features and the number of
variants within the considered family enlarges the solution
space. The problem in this study is an NP hard problem that is
why mathematical optimization models, such as the model in
[26], are not able to find the optimal solution for large solution
spaces in a reasonable time. GAs are capable of finding
solution to NP hard problems in a reasonable time [27]. GAs
are the most powerful unbiased optimization techniques for
sampling a large solution space [28]. Thus, GA-based model is
used to find optimum or near optimum product platform for
product family with large numbers of product variants and
features. GA is a search heuristic that mimics the natural
selection process.
4.1. Chromosome representation
Fig. 1. Additive/Subtractive Product Platform.
A chromosome representation of each individual in the GA
population plays an essential role in the selection of the genetic
operators and has a direct effect on the problem structure. Each
chromosome mirrors a possible product platform and is made
up of a sequence of genes. Each gene represents one of the
features of the product family that takes a binary value (0 or 1).
If the feature is found in the possible product platform, then the
gene value is 1, while the value zero is assigned to the gene
when the feature is not included in the possible product
platform. Fig. 3 illustrates the chromosome representation for
a possible product platform for a product family of total five
(5) features. The possible product platform in Fig. 3 consists of
the 2nd, 3rd and 5th features.
Fig. 2. IDEF0 for the proposed work.
Fig. 2 shows an IDEF0 diagram that summarizes the
proposed work. The inputs are each product variant
decomposed into the product family features, the demand for
each variant and the features precedence Feature precedence is
the logical and technological sequential relationships between
the features. For instance, if feature A is built on top of feature
B, then feature B must exist first before adding feature A. Other
inputs are the costs of the mass-producing, additive and
subtractive manufacturing of the product family features. The
constraints are the capabilities of the additive manufacturing
(i.e. the building direction, number of material in a single build,
surface roughness, part volume constraint and ability to build
overhanging features building direction) and the subtractive
manufacturing (i.e. machine axes and working envelope
dimensions). The mechanism is the GA-based model that is
detailed in the next section. The output is the product platform
that can be further manufactured by additive and/or subtractive
manufacturing into different product variants.
Fig. 3. Example of possible product platform chromosome.
4.2. Initial population
The initial population is made of a set of possible product
platforms. These possible product platforms consist of
randomly generated binary values (0 and 1) for each feature.
Afterwards, the feasibility of each possible product platform is
checked. The feasibility check is performed by making sure
that if feature A is dependent on feature B and feature A is
included in the platform, then feature B must be included in the
platform as well. If the dependent feature (A) takes value 1
then feature (B), which feature A depends on, must be 1. This
feasibility check is also performed between each two
successive generations (i.e. after applying the crossover and
mutation).
391
392
Mostafa Moussa et al. / Procedia CIRP 93 (2020) 389–394
4
Author name / Procedia CIRP 00 (2019) 000–000
4.3. Fitness function
4.5. Crossover
The fitness function is used to calculate the total
manufacturing cost for the product family. The lowest total
manufacturing cost the better fitness of the possible product
platform (solution). The fitness function is represented in
equation 1. The first term represents the cost of having features
in the product platform. The second term is for the cost of
adding features to the platform to form the required quantity of
each variant, while the third term is for the cost of removing
features from the product platform to obtain the demand of each
variant.
The crossover is the genetic operator responsible for
combining the features from two-parent possible product
platforms from one generation to obtain a new offspring
possible product platform. The single point crossover, which is
one of the popular crossovers, is applied. It can be summarized
as a point is randomly selected on both parent product
platforms, then the features fall on the right of that point are
swapped between the two-parent product platforms. The results
of this crossover are two offspring product platforms; each
carrying some features from both parents. Fig. 4 shows an
example for the single point crossover applied to two-parent
product platforms. Each product platform is formed of 7
features, the random point falls between the 4th and 5th features.
�
�� � ∑�
����∑��� Cp�
�
∑��� Cr� r�� D� �
�
x� D� � ∑��� Ca� a�� D� �
(1)
where,
TC
Total Manufacturing cost
K
the set of product variants in the product family, k ∈
K.
J
the features set, j ∈ J.
the demand of the kth product variant (units).
Dk
the cost of mass production of the jth feature using a
Cpj
platform.
the cost of adding the jth feature/material to form a
Caj
product variant (Caj>Cpj)
the cost of removing the jth feature/material (Crj >
Crj
Cpj) from the platform to form a product variant
xj
to indicate that feature j is included in the platform;
ajk
to denote that feature j is added to the platform to
customize it to form product variant k;
1 if the platform i contains feature j
x� � �
0 otherwise
a��
1 if feature j is added to the platform to form product
� � variant k
0 otherwise
rjk
r��
Fig. 4. Single point crossover applied to two-parent product platforms.
4.6. Mutation
The mutation is the genetic operator used for preserving and
introducing diversity within GA. The flip bit mutation, in
which the value of one or more randomly selected features’
positions within a parent product platform is inverted (i.e. if the
feature value is 1, it is changed to 0 and vice versa), is applied.
This results in an offspring product platform with one or more
features are altered from its parent. Fig. 5 shows an example
for the flip bit mutation applied to a parent product platform
includes 8 features. The 3rd, 5th and 8th features are randomly
selected.
to show that feature k is removed from the platform to
customize to form product k.
1 if feature j is removed from the platform to form
� � product variant k
0 otherwise
4.4. Selection
The tournament method is used to choose the possible
product platform for later using the genetic operators;
crossover and mutation. It can be described as two possible
product platform from a population are randomly chosen. The
possible product platform with the least total manufacturing
cost among the both is considered as a winner of the
tournament and used in the crossover and mutation.
3rd
1
1
0
5th
1
1
8th
0
0
0
0
1
Parent Product Platform
1
1
1
1
0
0
Offspring Product Platform
Fig. 5. Flip bit mutation applied to a parent product platform.
4.7. Stopping conditions
The process of the genetic search is repeated until the best
(elite) possible product platform are remained unchanged for
1000 generations.
Finally, the developed model is implemented using
MATLAB® programming and numerical computational
software.
Mostafa Moussa et al. / Procedia CIRP 93 (2020) 389–394
5
Author name / Procedia CIRP 00 (2019) 000–000
5. Illustrative Example
Table 2. Features precedence and the costs for adding, removing and massproducing each feature.
Consider an example of 14 product variants (V1 to V14)
involving a total of 12 different features (F1 to F12). The
product variants are shown in Fig. 6 and the product variantfeature incidence matrix is given in Table 1. The features
precedence and the costs for adding, removing and massproducing each feature are presented in Table 2. The
corresponding costs for using each manufacturing
method/technology (mass production, additive and subtractive)
are assumed based on the cost study of Manogharan et al. [29].
Feature
Precedence
Cp
Ca
Cr
F1
-
1
5
1.1
F2
F1
0.45
2.25
0.50
F3
F1
0.45
2.25
0.50
F4
F1
0.45
2.25
0.50
F5
F6
0.65
3.25
0.70
F6
-
1
5
1.1
F7
F6
0.65
3.25
0.70
F8
F6
0.65
3.25
0.70
F9
F1
1
5
1.1
F10
F1,F6
0.75
3.75
0.80
F11
F1,F6
0.75
3.75
0.80
Table 3. Demand Scenarios for the product variants.
Variant
Fig. 6. Product variants for the illustrative example.
Table 1. Product Variant-Feature incidence matrix of the illustrative example.
F1
F2
F3
V1
1
1
1
V2
1
V3
1
V4
1
V5
1
V6
1
1
1
V7
1
1
1
V8
1
1
1
V9
1
V10
1
V11
1
V12
1
F4
F5
F7
F8
F9
F10
F11
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
3
V1
142
50
200
V2
142
100
25
V3
142
25
45
V4
142
50
25
V5
142
23
100
V6
142
15
75
V7
142
75
200
V8
142
100
50
V9
142
500
18
V10
142
450
150
V11
142
75
20
V12
142
450
80
V13
142
50
500
V14
142
25
500
Total
1988
1988
1988
Table 4. Product platform and minimum total cost for each scenario.
1
1
V13
V14
F6
Demand Scenario
1
1
1
Scenario
Product Platform Features
1
F1, F2,F3,F5,F6,F7,F8,F9
20788.8
2
F1,F3, F6, F7,F9,F10
17532.35
3
F1,F3,F6 F7,F8
17548.95
1
Various cases of demand scenarios are examined to
illustrate the sensitivity of the product platform to the change
in the demand of the product variants. Three demand scenarios
are examined and shown in Table 3.
The optimum product platform and the minimum total cost
for each scenario were obtained in 2 seconds on a PC of Intel
Core i7 3.40 GHz processor and 16 GB Ram and are presented
in Table 4. Fig. 7 shows the designed product platform for each
scenario.
Fig. 7. Product platforms for each scenario.
Total Cost
393
394
6
Mostafa Moussa et al. / Procedia CIRP 93 (2020) 389–394
Author name / Procedia CIRP 00 (2019) 000–000
By conducting a comparison between both the mathematical
model in [26] and the proposed GA-based model, both models
were able to optimally solve the considered scenarios.
However, there was a huge reduction of computational time of
around 98% when the GA-based model was used. Thus, the
GA-based model can handle larger problems in much less
computational time than the mathematical model with the
increase in the number of variants and features.
6. Conclusions
This paper addressed the design of a product platform that
can be customized by additive and subtractive manufacturing
to obtain product variants which fall within a considered
product family. A GA-based model is developed for that
purpose while minimizing the total cost of mass-producing the
product platform and the further customization costs of the
product platform to produce the demand of the different
product variants. Furthermore, since the proposed model is a
meta-heuristic algorithm, it can overcome the limited
computational capacity of mathematical programming model
presented earlier by ElMaraghy and Moussa [26]. Thus, it can
handle practical problem sizes (i.e. product family formed of
tens of product variants with tens of features).
Applying the proposed product platform design model
should improve the flexibility, responsiveness and productivity
of the manufacturing system and hence support cost-effective
production. A quantitative assessment of the achievable
benefits through the proposed model is currently being studied.
Inventory management can be considered in the future work by
adding representative terms to the objective function. Only a
single product platform was analyzed with this GA-based
model. The demand of all product variants is limited to a single
production period.
Acknowledgements
Funding of the presented research project by the Natural
Sciences and Engineering Research Council of Canada
(NSERC) is acknowledged.
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