The international journal of advanced manufacturing technology, tập 59, số 5 8, 2012

Int J Adv Manuf Technol (2012) 59:421–432
DOI 10.1007/s00170-011-3516-y

ORIGINAL ARTICLE

A model to build manufacturing process chains
during embodiment design phases
Robert Blanch & Ines Ferrer &
Maria Luisa Garcia-Romeu

Received: 1 February 2011 / Accepted: 4 July 2011 / Published online: 22 July 2011
# Springer-Verlag London Limited 2011

Abstract The methods for manufacturing process selection
from early design phases avoid later mistakes and ensure
the success during product manufacturing. Currently, the
majority of the products need more than one manufacturing
process to become finished parts. This is known as a
manufacturing processes chain, and it is important that this
manufacturing chain is well designed. This paper presents
the bases and the activity model (IDEFØ) to develop a
decision-support system that helps designers and manufacturing engineers to configure manufacturing process chains
while the product is being designed. The model schematizes all the activities and information involved in obtaining
reliable manufacturing process chains. The support system
has been applied to an air-bending die design process to be
used to perform either air-bending or bottoming.
Keywords Manufacturing process . Process selection .
Activity model . Decision-support system

1 Introduction

In a context of profound changes in industrial markets—in
relation to globalization and delocalization—the main
R. Blanch : I. Ferrer (*) : M. L. Garcia-Romeu
Department of Mechanical Engineering and Industrial
Construction, University of Girona,
Campus Montilivi P-II,
Girona 17071, Spain
e-mail:
R. Blanch
e-mail:
M. L. Garcia-Romeu
e-mail:

challenge for all industries is to remain competitive [1]. In
this context, companies need to focus on satisfying as much
as possible the product requirements demanded by the
market. During the first stage of product development—the
design process—many decisions are made to meet these
requirements; however, such decisions also affect on other
issues, such as process planning, manufacturing, assembly
or recycling of the product. Considering these issues during
the design stage is important because wrong decisions can
have serious effects on development time, cost, and product
quality [2, 3]. Given that manufacturing issues must be
taken into account at the initial stages of design [3, 4], the
designer should know the manufacturing processes or
sequence of processes (i.e., the manufacturing process
chain) that may be used to manufacture what they are
designing. This, however, is not an easy task. First, there is
a large variety of manufacturing processes; second, the

knowledge associated with each process is abundant; and,
finally, the increasing trend towards relocating and
separating manufacturing and design centers from each
other has led to a decline in designers’ understanding of
manufacturing processes by making them less accessible.
To solve this problem, several methods and tools have
been developed to help designers select suitable manufacturing processes during product design.
Manufacturing process selection tools help designers
choose the most technically and economically suitable
manufacturing process to obtain a product [3, 5]. Most of
the work developed is based on quantitative analysis. In
manufacturing process selection-based on quantitative
analysis (MPS-BQA), the choice is made by comparing
the design parameters or specifications with the attributes of
the manufacturing process. Process attributes describe the
capabilities of the process in terms of material, shape, size,


422

tolerances, production rate, cost, and environmental impact,
allowing direct, objective comparisons to be made [5], for
example, of the tolerance or roughness each process is able
to obtain in a part. Some relevant examples of these
research studies are: CES [6], MAS 2.0 [7], WiSeProM [8],
and WebMCSS [9]. These tools may be applied from the
preliminary design stages, in which there is already a
rough idea of design parameters, such as shape, material
and weight, as well as of product restrictions, such as
production volume or cost limit. The tools result is a list

of manufacturing processes which are able to achieve the
basic product form but designer have to chose only one
manufacturing process option (a, b, c, d, and e in Fig. 1)
without combining more than one process as a chain
derivation allows. To obtain manufacturing process chain,
two basic requirements have to be considered. First, how
much and in what way a product is modified during each
process in the manufacturing process chain needs to be
considered, thus revealing what remains to be done in the
following processes. Second, the compatibility of different
manufacturing processes needs to be considered to develop
manufacturing process chains that are technically feasible.
This means ensuring that a particular process is compatible
with the subsequent process.
The process chain can be defined from early design
using a selection process or during detail design using a
configuration process (Fig. 1). The manufacturing process
chain selection comprises all the manufacturing processes—
taken as a sequence of processes—that meet all the product
requirements [10]. For example, chains I and II in Fig. 1.
On the other hand, configuring the manufacturing process
chain means choosing the machinery, tools, and other
production parameters that will meet the product quality

Fig. 1 Manufacturing
process chain related to the
design process

Int J Adv Manuf Technol (2012) 59:421–432

requirements [10] (see chains III and IV in Fig. 1.)
Therefore, the configuration takes place at the process
planning level.
This research is intended to develop a decision-support
system to help designers or manufacturer engineers know
the sets of manufacturing process chains that could be used
to manufacture the products being designed during early
design. It is assumed that each chain is able to manufacture
the product in its entirety. However, the paper presents the
first stages in the development of this system. First of all,
the framework approach on which the system is based is
described. Second, the IDEFØ activity model, in which all
the activities, information, and knowledge involved in
obtaining a set of viable manufacturing process chains are
gathered, is presented to help select manufacturing process
chains. Finally, an example that shows the application of the
model is explained in detail.
There are three main advantages of such a method. First,
the design parameters are better adapted to the manufacturing requirements and there is a better validation of the
manufacturability of the design for all the processes
involved in its manufacture; second, any problems during
the manufacturing phase arising from an unsuitable design
are reduced since these problems are detected during
the design process; and, finally, production costs can be
calculated and compared for various manufacturing
process chains.

2 Framework approach
The manufacturing process chain is defined as a process
map that describes how the initial product blank is

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transformed into the final product. To get a manufacturing
process chain capable of producing a product, a manufacturing process chain derivation method is used, which is
the core of the method described in this research study
(Fig. 2). The derivation method which will be presented
next is based on both design information and the
capabilities of the processes for transforming the products,
and provides as a result the set of viable manufacturing
process chains that will produce the product. It is focused
on mechanical products.
As shown in Fig. 2, the manufacturing process chain
must begin to take shape during the embodiment design
phase [9, 11], when the requirements and the functionality
are defined, and a preliminary draft is written. All this
design information has to be compiled in the product design
parameters, which are a qualitative description of the
designed product. Basically, these parameters have been
extracted from research works related to MPS-BQA [6, 7,
12], but they have been classified into three lists: required,
optional, and feature design parameters, which are
explained in detail in section 3.
The manufacturing process chain derivation method
requires concise information about the manufacturing

Fig. 2 Manufacturing process chain derivation

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processes, especially regarding their capacity to modify
the product with respect to the design parameters. This
information has to be comparable with the product
information in order to create viable manufacturing process
chains from a technological point of view. The manufacturing process description is divided into three parts
(Fig. 2):
&

The manufacturing process information concerns manufacturing process data related to product design and is
divided into manufacturing process constraints and
manufacturing process transformation capabilities.
–

The manufacturing process constraints are attributes that describe the manufacturing processes and
their ability to meet the product design parameters.
These constraints include process capabilities related
to material, shape, geometrical dimensions (e.g.,
thickness or tolerance), roughness, geometrical features, and production rates, which also define the
product, allowing direct and objective comparisons to
be made between design and manufacturing information. They are, therefore, the same as process
attributes defined by Lovatt and Shercliff [5].


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–

&

&

The manufacturing process transformation capabilities represent the capability of each manufacturing
process to modify the product design parameters
from the initial stage or to modify the product design
parameters that have been modified by previous
manufacturing processes. These capabilities are
defined using maximum values of transformation,
which quantify how much a manufacturing process
can change a product parameter. Furthermore, the
differences regarding manufacturing process constraints will be discussed further.

The manufacturing process sequencing rules define
technological constraints among different manufacturing processes so that it is possible to distinguish
between viable and non-viable manufacturing process
chains, because not all process combinations are viable
as a manufacturing process chain [11]. Therefore, for
each manufacturing process, it needs to specify all the
other compatible manufacturing processes that can be
carried out before it, after it, or both (Fig. 3). Figure 3
shows an example of the sequencing rules for the
milling process. It shows that during the manufacturing
of a part, the processes of casting and powder
metallurgy must always take place before milling,
whereas bending or drilling processes (labeled “both”
in the figure) can take place either before or after
milling. The polishing process, however, must take
place after milling.

The manufacturing process classification classifies
manufacturing processes that vary according to the
objective pursued with this classification. The manufacturing process classification proposed by Lovatt and
Shercliff [5] is used in this work. The processes are
classified according to the extent to which they can
transform the part and are classified as [5]: primary,
secondary, and tertiary. The “primary processes” take
unshaped material (liquid metal, or a powder, or a solid
ingot) and give it shape. Thus, molding, casting or
machining processes are primary. The “secondary
processes” modify, add, or refine features to an
already-shaped body, such as fine machining and

Fig. 3 Example of milling
process sequencing rules

polishing. And finally, the “tertiary processes” add
quality either to the bulk or to the surface of a
component, for example, shot-peening of surfaces.
Although this classification is not absolute, since a
particular process, such as machining, may belong to
more than one group, the use of this process classification reduces the complexity of the problem and limits
the number of candidate processes for manufacturing
at each level of the product design. Therefore, it limits
the number of processes that need to be analyzed in
order to configure each step of the manufacturing
process chain.

3 Process chain derivation model
Modeling knowledge and information used to integrate

design information with manufacturing information has
been extensively studied and is still a very active field, as
confirmed by the following studies. Skander et al. [1]
modeled all the product information, the manufacturing
constraints related to design, and the required rules to
implement a method that integrated process selection and
manufacturing constraints into the design. Ferrer et al. [13]
proposed a method to formalize the most relevant design
information related to manufacturing that should be made
available to the designer to design for manufacturing of
new designs. Ciurana et al. [14] modeled the process
planning activities in sheet metal processes and the model
was implemented in a computer-aided tool. Guerra-Zubiaga
and Young [15] show different ways to model manufacturing knowledge and how to make it available when needed.
Thibault et al. [16] propose an integrated product–process
approach to evaluate its consistency and is useful in
selecting suitable forging process and product design
parameters. Yuh-Jen Chen [17] modeled the process for
conventional molding product design and process development by using the process modeling technique IDEFØ. And
finally, Mauchanda et al. [18] model the knowledge and
information to develop a tool to calculate the manufacturing
cost from conceptual design.


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In accordance with the framework approach presented in
Section 2, an activities model using IDEFØ methodology
has been developed as skeleton of a decision-support
system to obtain a process chain. The purpose is to

schematize all the activities involved in obtaining the viable
manufacturing process chains to manufacture a given
design from the designer’s point of view, i.e., to derive
the process chain (Fig. 4).
The inputs required to carry out the main activity are the
computer-aided design (CAD) part sketch and the manufacturing process pool, whereas the output will be the set of
viable manufacturing process chains. Manufacturing process information, manufacturing process sequencing rules,
and manufacturing process classification act as controls.
The manufacturing process pool represents the whole set of
manufacturing processes that are considered for the
selection. It may be wider or narrower depending on the
scope. This main activity, A0, is broken down into four
specific activities, A1, A2, A3, and A4, shown in Fig. 4,
which will now be described in detail.
Activity A1. “Analyze the product”
In this activity, the designer has to analyze
the product information from the CAD part
sketch and classify it into three lists of
design parameters (see Fig. 5): required,
optional, and feature design parameters. In
this way, the design parameters are organized
in terms of how they can be obtained by the
manufacturing processes that will form part
of the chain, which is important to establish
process chains. The first list consists of the
required design parameters, which are those
that all the manufacturing processes in the
process chain have to be able to deal with.
These parameters are exclusive, which
means that a process is excluded when it is

not able to process with this property, for any
step of the process chain. The second list is
the optional design parameters, which are
Fig. 4 The basic derivation
of the process chain A0

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product parameters that may be transformed
by various manufacturing processes until the
final optional design parameter is reached.
Finally, the third list is the feature design
parameters, where a feature refers to the
significant processing of portions of the
geometric shape of a part or assembly.
Neither optional nor features are exclusive
because they can be obtained along the
process chain.
Activity A2. “Analyze and select manufacturing process
level 1”
The goal of this activity is to analyze and
select the first manufacturing process in the
process chain from the manufacturing process pool, using the required, optional and
feature design parameters as inputs, and
both manufacturing process information and
manufacturing process classification as controls (Fig. 5). Two outputs are obtained: a set
of manufacturing processes ranked according to which should occupy the first position
of the process chain, called manufacturing
process ranking for level 1, and a list of
resolved/unresolved design parameters. The

resolved design parameters are those which
will have been completely transformed or
changed by the selected process whereas the
unresolved design parameters are those
which will require further manufacturing
processes. Activity A2 is further broken
down into four sub-activities, shown in
Fig. 6.
A2.1. “Select manufacturing processes compatible
with the material”
The inputs for this sub-activity are the
manufacturing process pool and the material
design parameter. The material of the product is
compared to the set of materials with which


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Int J Adv Manuf Technol (2012) 59:421–432

Fig. 5 Detailed derivation of the process chain

each process is able to work, thus the result
obtained is a list of manufacturing processes
compatible with the material. The material for
required design parameters was chosen as the
first discriminatory step because this parameter
is the most restrictive in terms of selecting
manufacturing processes and it reduces the
search range for the next steps [5, 9]. It means

that choosing the material for the first step a lot
of processes can be excluded since the product
cannot be obtained.
A2.2. “Check required parameters”
This sub-activity checks whether or not the
processes in the list of manufacturing processes
compatible with the material (from activity
A2.1) are able to manufacture the other required
design parameters. These parameters are compared to the manufacturing process constraints
of each process. When the process is able to
obtain all the parameters from the list of
required design parameters then the process is
kept on the list; otherwise it is excluded. The
result is the list of manufacturing processes
satisfying required properties.
A2.3. “Check optional and feature parameters”
In this activity the lists of optional and
feature design parameters are checked. The
result is the viable manufacturing process list
and a first version of the list of resolved/
unresolved design parameters indicating which
processes are able to transform the part according to those parameters and which ones are not.

A2.4. “Evaluate the manufacturing process
transformation”
As stated in Section 2, transformation is the
capability of each manufacturing process to
modify the parameters of the product either
from the initial stage or after a previous
manufacturing process has already modified

them. It means that achieving the values of a
given parameter depends on the starting value
of this parameter on the part. To evaluate the
manufacturing process transformation, the
method needs to calculate the transformation
required in the product parameters by comparing the status of these parameters from one
manufacturing process to the next. Subsequently,
the values obtained for the required product
transformation must be compared with the
transformation capabilities of the particular
manufacturing process. When the calculated
values are less than or equal to the manufacturing
process transformation capabilities, the manufacturing process is deemed able to transform all
the “resolved design parameters” of the part and
therefore there is no need to update the list of
resolved/unresolved design parameters. Otherwise, when the calculated values are greater
than the manufacturing process transformation
capability, the list of resolved/unresolved design
parameters will be updated accordingly.
A2.5. “Estimate the manufacturing cost”
In the fifth and last sub-activity of A2, the
viable manufacturing processes are ranked


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427

Fig. 6 Details of Activity A2—analysis and selection of manufacturing process level 1

according to economical criterion. Several
methods have been developed for manufacturing cost estimation from early design stages, for
example CES [6] and Swift and Booker [19]
method. These methods are based on three main
elements: material and consumables, tooling
and equipments, and investment, where the
batch size becomes a key factor. Depending on
the value of the batch size the manufacturing
cost changes considerably. In addition, some
processes that may be viable from a technological point of view become non-viable from an
economical point of view depending on the
batch size.
When A2 activity is complete, it might be
that a single manufacturing process can make
the entire part or, in contrast, that it is necessary

to continue building the chain of manufacturing
processes. This decision is determined by the
list of resolved/unresolved design parameters. If
all design parameters are resolved, the chain of
manufacturing processes is complete and activity
A4 will be implemented, showing the first
result. If they are “unresolved” and there are
still some parameters that have not been
achieved or only partly achieved, activity A3
continues the elaboration of the chain of
manufacturing processes until all the design
parameters are resolved.
Activity A3. “Analyze and select process level n (A3)”
In this activity, the manufacturing process ranking for level 1 from the activity

(A2) is used to evaluate new manufacturing
processes for the next step in the process chain.


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In addition, a new control is used: manufacturing process sequencing rules. These rules
validate the technological feasibility of each
combination of manufacturing processes. Although the procedure of this activity is similar
to that of the previous activity (A2), there are
two main differences. The first change is the
starting point, since now it has the list of
resolved and unresolved parameters from the
previous activity, representing the design properties carried out by the previous process and
those pending in the next one. This list will be
updated until the manufacturing process chain
resolves all the unresolved parameters. The
second difference is that the transformation
calculation is carried out using the lists of
resolved and unresolved parameters from the

Fig. 7 CAD sketch of the die used in the case study

previous manufacturing process as well as the
process currently being checked.
Activity A4. “View final process chain (A4)”
This activity provides a list detailing the
selected manufacturing processes that make

up the process chain.

4 Application of the proposed model
The developed model was applied to a selected set of
mechanical parts. However, in this work the design process
of an air-bending die (Fig. 7) to be used to perform either
air-bending or bottoming is discussed in detail. The
manufacturing processes are reduced in this sample to
“powder metallurgy”, “machining”, “polishing”, “hot
closed die forging”, and “roll forming”. Nevertheless, the


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429

model developed is also applicable for other mechanical
parts than this sample and whole manufacturing processes
feasible for mechanical parts being manufactured. Following the proposed IDEFØ diagram and based on the current
version of the “CAD part sketch” (Fig. 7), the designer or
manufacturing engineer has to extract the design parameters
and classify them into required, optional and feature design
parameters. Table 1 shows these three lists and the values of
the parameters for the case study. The lists are produced
during activity A1, as shown in Fig. 5.
In activity A2 (Fig. 5), the lists of product design
parameters from Table 1 and the manufacturing process
pool are used to produce two outputs. The first one is the list
of processes that can be used as the first manufacturing
process of the process chain which will initiate production of

the part, i.e., “hot closed die forging”, “powder metallurgy”,
and “machining”. The second output is the list of resolved/
unresolved parameters, which it will be explained later.
Nevertheless, to achieve these outputs, the A2 sub-activities
must first be carried out. Figure 8 shows in detail the results
of these A2 sub-activities for the die case study.
Initially, the A2.1 sub-activity gives a list of all the
manufacturing processes capable of working with the
material of the product in question, comparing the product
material with the set of materials that each process is able
to manufacture. “Hot closed die forging”, “powder
metallurgy”, “machining”, and “roll-forming” make up
the list of manufacturing processes compatible with the
Table 1 Product design parameters of the case study
Product design parameters
List

Parameter

Value

Required design
parameters

Material

Iron

Shape
Length (X)

Width (Y)

Prismatic–nonaxisymmetric–solid
[75; 75] mm
[115; 115] mm

Height (Z)
Weight
General roughness
Specific roughness
General tolerance
Specific tolerance
Corner radius
Type
Diameter
Height (Z)
Roughness
Tolerance

[24; 55] mm
3 kg
10 μm
5 μm
10
0.5
1
Hole
8.5 mm
55 mm
0.1 μm

0.002

Optional design
parameters

Feature design
parameters

material. Subsequently, these processes are further filtered
by sub-activities A2.2 and A2.3. Activity A2.2 checks the
list of manufacturing processes compatible with the
material to see which ones satisfy the other required
parameters, which in the example are weight and height.
Both are numeric parameters and it is checked that its value
is included in the range of values that each process is able
to achieve, according to its manufacturing process constraint. The “hot closed die forging”, “powder metallurgy”,
and “machining” processes meet these requirements and are
therefore allowed to continue as input for the next activity,
A2.3, In contrast, the “roll forming” process cannot achieve
the required height and is removed from the list. Now,
activity A2.3 checks the list to see if these processes are
capable of manufacturing the optional and feature design
parameters, which in this case include general roughness,
specific roughness, and hole.
As shown in Fig. 8, the process “hot closed die forging”
can meet the material, weight, general roughness and
height requirements, but not the specific roughness and
hole requirements. Choosing this process would require a
subsequent manufacturing process to complete the part.
In contrast, “machining” is able to resolve all the design

parameters, which suggests that, for this case study, this
process would be sufficient to produce the part. However,
following the model proposed here, it is necessary to
analyze whether each process can transform the objectives set out in the list of design parameters (sub-activity
A2.4).
At this point, the method has evaluated the capacity of
the processes to meet the product design parameters taking
into account the manufacturing process constraints. However, activity A2.4 assesses the capability of the manufacturing processes to transform the parameters from the
output list of activity A2.3. Figure 9 shows the results of
activity A2.4 for the process “hot closed die forging” Fig. 9.
The process “hot closed die forging” has to transform the
parameters of weight, height, and general roughness from
an initial status (previous step) to a final status (next step).
In that case, the initial status corresponds to the material
blank, which is considered as the volumetric space of the
part. Therefore, the values for weight and height take it into
account. The final values of these parameters appear in the
next step. The parameters are quantified with a numerical
value—as the weight—or using a range that shows the
maximum and minimum values the parameter takes in the
part—as the height dimension. The result of this transformation is described in the product transformation needed
column in the product transformation table. The resulting
values must then be compared with the range of transformation values found for “hot closed die forging” in the list
of manufacturing process transformation capabilities. The


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Fig. 8 Flow chart of Activity A2

result of this comparison is shown in the transformation
result table, which notes whether or not the process “hot
closed die forging” can sufficiently modify the parameters
of the product. If a parameter cannot be transformed by the

manufacturing process, such as height in this case, its value
is adapted in relation to the transformation capacity of the
process. In this case, the “hot closed die forging process”
cannot reduce the height of the part from an initial 55 mm


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431

Fig. 9 Evaluating the manufacturing process transformation capability (A2.4)

to a final 24 mm because the maximum process transformation capability for height is 25 mm. Therefore, after this
process the height of the part will be 30 instead of 24 mm.
Thus, this parameter, which seemed to be resolved at the
beginning of activity A2.4, in resolved/unresolved design
parameters, version 1 (Fig. 9), is classified as unresolved at
the end of it. When a parameter such as roughness can be
transformed by the process, it is kept as “resolved” in the
list. The weight parameter is not affected by this manufacturing process capability. The outputs of Activity A2.4 are,
first, an update that gives us resolved/unresolved design
parameters (version 2) for each manufacturing process and,
second, the list of selected manufacturing processes, as

shown in Fig. 8.
Next the manufacturing cost is estimated according to
the batch size. Considering the Swift and Booker cost
method [19] when the batch size is lower than 1,000 units
only manual machining is viable and the estimated cost is
21 € per part. Neither “hot closed die forging” nor “powder
metallurgy” are viable from economical point of view.
Otherwise when is higher than 1,000 units “hot closed die
forging”, “powder metallurgy”, and “automated machining”
continuing being viable. The estimated costs for 10,000
units are 30, 97.6, and 13.7 € per part, respectively, whereas
for 50,000 units the cost is 12, 27.6, and 7.9 € per part,
respectively. Figure 8 shows the processes ranked according
to this result.
At this point, the first manufacturing process chain for
the manufacture of the die, consisting simply of “machining”, is achieved. However, for the manufacturing processes
with unresolved parameters, the manufacturing process
chain must continue to be constructed. This means
carrying out Activity A3 which, in the case of “powder
metallurgy” produces the chain “powder metallurgy–
machining–polishing” and in the case of “hot closed die
forging” produces the chain “hot closed die forging–
machining–polishing”.

5 Conclusion
This paper presents the bases for developing a decisionsupport system that would help designers and manufacturing engineers know which manufacturing process chains
can be used to manufacture a product. The main research
contribution of this work is to help designers to define the
set of useful manufacturing processes chains thus the
designer could select for manufacturing a mechanical part

based on cost estimation and technical feasibility. Then
result is based on showing all the activities, information,
and knowledge involved in obtaining a set of viable
manufacturing process chains to manufacture a product
through the method by utilizing IDEFØ. To reach this
purpose detailed novelties are:
&
&

&

&

The model makes it possible to control the properties
modified in each step of the process chain and to know
if the properties are partially or completely obtained.
New classification of design properties identifying
those which are exclusive (required properties) and
those which are not (optional and feature properties),
and a procedure to assess the manufacturing process
transformation capability have been proposed.
Definition of manufacturing sequencing rules to consider the compatibility among manufacturing processes to obtain viable manufacturing process chains
is created.
The research is validated by applying the method to an
air-bending die case study

The proposed model certainly makes it easier to develop
manufacturing process chains; however, the next step in this
research is focused on the development of a decisionsupport system to select the manufacturing process chain.
The model should be integrated in a CAD system making

the model useful, reliable and feasible for industrial
application.


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Int J Adv Manuf Technol (2012) 59:433–443
DOI 10.1007/s00170-011-3514-0

ORIGINAL ARTICLE

A feasibility study using simulation-based optimization
and Taguchi experimental design method for material
handling—transfer system in the automobile industry
Kemal Subulan & Mehmet Cakmakci

Received: 8 November 2010 / Accepted: 4 July 2011 / Published online: 28 July 2011
# Springer-Verlag London Limited 2011

Abstract Nowadays, so as to adapt to the global market,
where competition is getting tougher, firms producing through
the modern production approach need to bring not the only
performance of the system designed both during the research
and development phase and the production phase but also the
performance of the product to be developed as well as the
process to be improved to the highest level. The Taguchi
method is an experimental design technique seeking to
minimize the effect of uncontrollable factors, using orthogonal
arrays. It can also be designed as a set of plans showing the way
data are collected through experiments. Experiments are carried
out using factors defined at different levels and a solution model
generated in ARENA 3.0 program using SIMAN, which is a
simulation language. Many experimental investigations reveal
that the speed and capacity of automated-guided vehicle, the
capacities of local depots, and the mean time between shipping
from the main depot are the major influential parameters that
affect the performance criteria of the storage system. For the
evaluation of experiment results and effects of related factors,
variance analysis and signal/noise ratio are used and the
experiments are carried out in MINITAB15 according to
Taguchi L16 scheme. The purpose of this study is to prove
that experimental design is an utilizable method not only for
product development and process improvement but it can also
be used effectively in the design of material handling–transfer
systems and performance optimization of automation technologies, which are to be integrated to the firms.
Keywords Taguchi experimental design . Material handling

and transfer systems . Performance optimization . Process
improvement
K. Subulan : M. Cakmakci (*)
Engineering Faculty Industrial Engineering Department,
Dokuz Eylül University,
Buca 35160 Izmir, Turkey
e-mail:

1 Introduction
In order to improve the process in production and optimize the
results obtained from production, it is required to increase the
production performance. To present the conditions for which
the optimum results are obtained in the design phase,
primarily, properties determining the performance level are
specified and factors affecting these properties are examined.
Following that, experiments are carried out in order to
determine the effects of these factors on properties setting
the performance and find the optimum combination (by also
observing the uncontrolled factors) [1].
Considering the basic production resources, the application of experiment design techniques becomes extremely
efficient for carrying out these experiments with the highest
efficiency pursuing the economical and time constraints as
well as interpreting the results accurately (so as to
determine the relationship between controllable–uncontrollable factors and outputs and to realize the optimization).
Besides, it has a supportive and directive role on all other
methods applied to increase quality and efficiency.
With the material handling–transfer equipment, performance increase is achieved in storage systems with
automation. Thus, long-term execution of material handling–transfer equipment within the system affects the
performance with respect to various storage system criteria
hence the performance increases in this respect (increasing

the amount of utilization rate of material handling–transfer
equipment and the minimization of average number of
products awaiting to be carried by the material handling–
transfer equipment and the average latency time) cost
reduction and optimum storage management [2].
The purpose of this study is to prove that the experimental
design is a method that can be used effectively in not only
product development and process improvement studies but
also in the designing of material handling–transfer systems


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Int J Adv Manuf Technol (2012) 59:433–443

Fig. 1 The 3-D layout of storage system

and performance optimization of automation technologies,
which are to be integrated into the operation.

the application of this technique in production, several
advantages are provided such as improvement of performance and quality, efficient use of the sources, acceleration
of the research and development activities, making process
or products less susceptible to factors, which are costly, hard
to control or uncontrollable, and affect quality properties.
Taguchi method is a technique for designing and
performing experiments to investigate processes where the
output depends on many factors (variables, inputs) without
having tediously and uneconomically run of the process
using all possible combinations of values. Thanks to

systematically chosen certain combinations of variables, it

2 Taguchi method and experimental design
Experimental design, which is used in the design phase before
manufacturing in production, is not only a statistical approach
but it is also a technique that can be used in research and
development activities and support and complete all other
quality techniques while minimizing the cost, enhancing the
quality, and reinforcing the reliability of the results [1, 2]. With

Table 1 From/to chart depicting
the distances between local
depots and main depot
inlet–outlet

From/to

Input area

X

Y

Z

T

Q

P

Shipping area

Input area
X
Y
Z
T
Q
P
Shipping area

0
22.5
12.5
22.5
62.5
72.5
62.5
100

22.5
0
15
25
45
55
65
82.5

12.5
15
0
15
55
65
55
90

22.5
25
15
0
65
55
45
80

62.5
45
55
65
0
15
25
62.5

72.5
55
65

55
15
0
15
72.5

62.5
65
55
45
25
15
0
62.5

100
82.5
90
80
62.5
72.5
62.5
0


Int J Adv Manuf Technol (2012) 59:433–443

435

Table 2 Loading and offloading times of AGV according to product

types (in seconds)
X

Y

Z

T

Q

P

Loading time

5

6

4

6

7

6

Unloading time

3

4

5

2

4

5

is possible to separate their individual effects [3, 4]. It can
be also defined as a set of plans describing the data
collection types with experiments. In designing an experiment, the general purpose of the problem, the input
variables to be examined and their levels, the reaction of
the experiment, the walkthrough of the experiment, and
appropriate analysis methods should be determined.
Dr. Genichi Taguchi is regarded as the foremost
proponent of robust parameter design, which is an
engineering method for product or process design that
focuses on minimizing variation and/or sensitivity to noise.
When used properly, Taguchi designs provide a powerful
and efficient method for designing products that operate
consistently and optimally over a variety of conditions. In
Taguchi’s methodology, all factors affecting the process
quality can be divided into two types: control factors and

Fig. 3 AGV-Automated guided vehicle [19]

noise factors. Control factors are those set by the

manufacturer and are easily adjustable. These factors are
most important in determining the quality of product
characteristics [5, 6].

Fig. 2 Simulation model that developed for the storage system– ARENA 3.0


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Int J Adv Manuf Technol (2012) 59:433–443

Table 3 Experimental factors and factor levels
Experimental
control
factors

A/speed
of AGV

B/capacity
of AGV

C/capacities
of local
depots

D/the mean
time between
shippings from
the main depot

(in seconds)

Level 1 (−1)

3

1

40

expo(120)

Level 2 (+1)

7

3

80

expo(180)

This technique is a technique that is used for determining
the variable values affecting the process performance by
systematically manipulating the controllable variables,
which affect the related quality characteristics [7-9].
Genichi Taguchi came up with a solution which
increases the efficiency of the evaluation and realization
of experiments with the help of the approach called with his
name [10]. Besides being only an experimental design

technique, Taguchi method is an extremely beneficial
technique for high-quality system design. On the other
hand, the reduction in the number of experiments stems
from ignoring the interaction between factors to some
extent. The experiment results obtained through Taguchi
experimental design method are converted into signal/noise
(S/N) ratio for evaluation. The value of signal/noise ratio is
calculated and analyzed in different ways such as low value
being good or high value being good or nominal value
being good, according to the targeted quality value.

Whichever S/N ratio value is used in evaluation, as a result
the higher S/N ratio value expresses the better experiment
result. Thus, the case with the highest S/N ratio among all
the factors examined within the experiment would give the
best performance [11, 12]. The standard designs of Taguchi
are built on this system. It is required that a model be
constructed for the variation of data on the targeted value.
For that purpose, loss function to calculate the deviation
between the experimental and the desired value is modelled
as below:
LðyÞ ¼ k ðy À T Þ2

ð1Þ

In this function, L(y) is the loss function, k is
proportional constant, T is target value, and y is observed
value. The data obtained through loss function, a formulation, which is expressed as S/N ratio, is developed by
Taguchi. With the help of S/N ratio, which is also
expressed as the variation of the process, optimum process

conditions which are used for the optimization of the
process are obtained. Factor levels with the highest S/N
ratios are the factor combinations providing the optimum
conditions [13].
S=N ¼ À10 logðLyÞ

ð2Þ

For S/N ratio, there are three different approaches. These
are smaller is better, larger is better, and target value is
better. For each approach, a different calculation scheme is
developed.

Table 4 Orthogonal array L16 of the experimental runs (experimental layout) and response values for the experiment
Standard
run no.

Randomized

A

B

C

D

The average utilization
rate of AGV(%)

The average number of
waiting products for AGV

The average waiting time of products
in the input area (in seconds)

1
2
3
4
5
6
7
8
9
10
11

7
3
10
2
15
1
9
4
14
5
12

1
1
1
1
1
1
1
1
2
2
2

1
1
1
1
2
2
2
2
1
1
1

1
1
2
2
1
1

2
2
1
1
2

1
2
1
2
1
2
1
2
1
2
1

91.90
64.87
98.60
75.42
88.86
62.83
98.32
72.92
49.83
35.20
55.52

25.701
2.4054
41.518
4.594
0.02662
0.01086
0.03409
0.01476
0.35922
0.1773
0.4393

1,029.9
108.09
1,656.9
206.22
1.0665
0.48933
1.3661
0.066526
14.396a
7.9911
17.613

12
13
14
15
16

8
16
6
11
13

2
2
2
2
2

1
2
2
2
2

2
1
1
2
2

2
1
2
1
2

41.22
48.62
34.45
54.05
40.23

0.22193
0.00261
0.00107
0.00338
0.00144

10.005
0.10508
0.004817
0.13566
0.06473


Int J Adv Manuf Technol (2012) 59:433–443

437

Table 5 S/N ratios of Taguchi experimental results
The average number of The average waiting time
The average
of products in the input
utilization rate of waiting products for
area (dB)
AGV (dB)

AGV(dB)
−0.74
−3.77

−89.005
−8.44

−61.025
−41.46

−0.12

−32.97

−64.95

−2.46
−1.20

−14.2
31.33

−47.19
−0.7

−4.05

39.1

−0.16

−2.75

29.25
36.5

−2.82
3.43

−6.06

8.84

−23.22

−9.08
−5.12

14.99
7.098

−17.9
−24.96

−7.71

13.04

−67.29

−6.27

−9.27

51.36
1.32

19.25
26.79

−5.35

49.164

17.08

−7.92

5.76

23.39

3 Definition of the problem and data

6.035

For smallest is better characteristic, function is defined as
Ly ¼ ðy12 þ y22 þ y32 þ . . . yn2Þ=n

ð3Þ

For target value is better characteristic, function is

defined as
Ly ¼ ððy1 À mÞ2 þ ðy2 À mÞ2 þ . . . ðyn À mÞ2Þ=n

ð4Þ

For largest is better characteristic, function is defined as
Ly ¼ ðð1=y12Þ þ ð1=y22Þ þ ð1=y32Þ þ . . . ð1=yn2ÞÞ=n

Fig. 4 The main factor effects
obtained from statistical analysis
for the average utilization rate of
AGV

In the experimental design, orthogonality is defined as
the calculability of a factor without being dependent on
another factor. The effect of a factor does not have an
influence on the estimation of the effect of another factor.
The first rule of orthogonal series is that they are balanced
experiments. In other words, they include equal number of
trials for different trial conditions.

ð5Þ

There are six different product types of ABC firm,
which produces in automotive industry. There are
specific locations within the main store where these
products are stored. In other words, there are six
different locations (local depots) for six different
products (Fig. 1). All products are moved to the main
depot entrance by the help of an overhead line. In the

main depot, products are stored in an automation-based
storage system, automated storage/retrieval systems (AS/
RS) which are called local depots. For the moment, in the
storage system, handling is realized using manual
handling cars and forklifts. In the direction of executives’
support, the feasibility studies are carried out for
transition of the manual storage system to automated
system.
The unit arrival percentages of six different product
types to the main depot entrance from overhead lines where
the final products are handled are as follows. Fifteen
percent of them is X-type, 20% is Y-type, 20% is Z-type,
15% is T-type, 20% is Q-type, and 10% is P-type. Similarly,
the unit outlet percentages are as follows respectively: 15%
is X-type, 20% is Y-type, 20% is Z-type, 15% is T-type,
20% is Q-type, and 10% is P-type. The automation to be


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Int J Adv Manuf Technol (2012) 59:433–443

Fig. 5 Two-factor interaction
effects obtained from statistical
analysis for the average utilization rate of AGV

applied to the material handling–transfer system is to be
designed according to the performance optimization of the
automated-guided vehicle (AGV) system, which would
provide the computer control of the handling operation

between the locations of differently packed products. The
designed AGV system is responsible for two types of
handling movements.
The first one is the handling of parts, which have
arrived at the main depot, to different locations; and
the other one is the handling of products, which will
be let out from the main depot, to the outlet of the

Fig. 6 The main factor effects
obtained from statistical analysis
for the average number of waiting products for AGV

main depot. All of these data are obtained through an
input analysis carried out in the ARENA program. The
number of AGVs and the number of products carried
by these vehicles at a time is taken as 1. Following
that, in the experimental design phase, the extent to
which these values have an effect on performance
criteria will be dealt with. The carrier goes to the
target location following the shortest path within the
main depot. This information provides us with the
construction of from/to chart for the main depot whose
layout is given in Fig. 1. The speed of AGV is specified


Int J Adv Manuf Technol (2012) 59:433–443

439

Fig. 7 Two-factor interaction

effects obtained from statistical
analysis for the average number
of waiting products for AGV

as 3 m/s. The AS/RS system, where every product is
stored, has a capacity of 40 products/unit and at the
starting moment it is assumed that the stock level on
these system is 0.
In Table 1, distance matrix (meter) which consists of
local depots, inlet, and outlets of the main depot regarding
the routes that AVG should follow using the layout of main
depot, and from/to chart can be seen. AGV times differ due
to the weights of products being transferred. The loading
and offloading of heavier products are executed slower due
to product sensitivity (Table 2). For the problem described
above, a simulation model, which simulates ABC firm

Fig. 8 The main factor effects
obtained from statistical analysis
for the average waiting time
of products in the input area

working three shifts, is run for 24 h and the obtained
performance criteria values are used as data in the
experiment design.

4 Experimental procedures and results
Simulation model developed in problem-specific ARENA
3.0 simulation program can be seen in Fig. 2. The values of
variables, which are determined as controllable factors,

within the model, are altered according to the determined
factor levels, and the model is run. In this way, the related


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Int J Adv Manuf Technol (2012) 59:433–443

Fig. 9 Two-factor interaction
effects obtained from statistical
analysis for the average
waiting time of products
in the input area

performance criteria values are obtained as response
variables within the experimental design.
Generally, the total response number is dependent on
the number of factors, factor levels, number of constrained factors, and number of experiment repetitions.
Especially, in multifactored experiments, the response
number may be very high. This situation increases the
cost as well as the time spent for experiments. For this
reason, in order to reduce the number of experiments and
cost, the number of responses is reduced. However, the
reduction of the response number may cause insufficient
data collection for the inspected event. Hence, responses
should be reduced without affecting the data collection
procedure.
In the consideration of all this information, in order to
specify the number of repetitions, the interval estimation
of the basically related performance criterion, beneficial

use ratio of AGV, is performed in ARENA program by
taking repetition number as 5 and following that, after
the desired half-interval level is specified, the necessary
observation number is determined using the following
equation [14].
 
 
»
» 2
n ¼ n Á h=h

ð6Þ

Under these circumstances, the total observation number
is specified as 20. As a result of the brainstorming sessions
held and the cause–effect matrices constructed in the firm,
several performance criteria such as average utilization rate
of AGV (see in Fig. 3), the average number of products
awaiting in the main depot’ input area to be transferred to
the local depots and average idle time of these parts are set

as the performance criteria used for specifying the activity
of the automation.
It is observed that these performance criteria are affected
by AGV speed, AGV capacity, local depots’ capacities, and
the intervals between the product transfers at the main
depot outlet (Table 3).
Before the application of the experiment, it is required that
a “receipt” table should be prepared for the team who will run
the experiment. The receipt table is constructed according to

the factor values which should be adjusted for every
observation and “+” and “−” signs of L16 design matrix [7, 8].
The experiment will be run by taking the repetition number
as 20 due to the facts explained before. For the repeated
experiments, response variables are assigned using the
average of results and point estimation. Hence, the reliability
of the results is provided statistically by repeating the
experiments for 20 times and averaging the results.
In Table 4, point estimation values for the average utilization
rate of AGV, average number of products awaiting for AGV
handling and average idle time for the awaiting product can be
seen. These values will be used in the calculation of signal/noise
ratio and variation analysis operations.

5 Evaluation of results
5.1 Taguchi method results
In Taguchi experimental design method, the criterion
used for measurement and evaluation of quality characteristics is the ratio of signal (S) to be measured to the
noise factor (N). Signal value is the actual value that the
system gives and desired to be measured, while noise


Int J Adv Manuf Technol (2012) 59:433–443
Table 6 ANOVA results for the
average utilization rate of AGV,
ANOVA results for the average
number of waiting products for
AGV, ANOVA results for the
average waiting time of products
in the input area

441
Degree of freedom

Mean square

F value

ANOVA results for the average utilization rate of AGV
A: Speed of AGV
5424.32

1

5424.32

9100.6

B: Capacity of AGV

9.42

1

9.42

15.81

C: Capacities of local stores
D: The mean time between shippings

222.9
1571.33

1
1

222.9
1571.33

373.98
2636.28

A×B

0.74

1

0.74

1.24

A×C
A×D

12.04
124.99

1

1

12.04
124.99

20.2
209.7

B×C

0.2

1

0.2

0.34

B×D
C×D

0.00
1.66

1
1

0.00
1.66

0.01
2.79

Residual

2.98

5

0.60

Source

Sum of squares

Total
7370.61
15
ANOVA results for the average number of waiting products for AGV
A: Speed of AGV

333.96

1

333.96

6.00

B: Capacity of AGV

C: Capacities of local stores

354.58
20.57

1
1

354.58
20.57

6.37
0.37

D: The mean time between shippings
A×B

229.96
332.54

1
1

229.96
332.54

4.13
5.98

A×C

A×D
B×C

20.01
223.89
20.52

1
1
1

20.01
223.89
20.52

0.36
4.02
0.37

B×D
C×D
Residual

229.37
11.68
278.14

1
1
5

229.37
11.68
55.63

4.12
0.21

Total
2055.21
15
ANOVA results for the average waiting time of products in the input area
A: Speed of AGV
545508
1

545,508

6.31

B: Capacity of AGV
C: Capacities of local stores
D: The mean time between shippings

580324
33387
356385

1
1

1

580,324
33387
356,385

6.72
0.39
4.12

A×B
A×C

543117
32431

1
1

543,117
32,431

6.29
0.38

A×D

347988

1

347,988

4.03

B×C
B×D
C×D

33292
355538
17570

1
1
1

33,292
355,538
17,570

0.39
4.11
0.2

Residual
Total

432024
3277562

5
15

86,405

factor is the undesired factor portion within the measured
signal value.
In the calculation of signal/noise ratio, the target quality
value which is desired to be reached at the end of the
experiment, is also important. At this point, three important
categories are available [1, 15-18]:
&

Lower value is better (target is to reach the lowest
value)

&
&

Higher value is better (target is to reach the highest
value.)
Nominal value is better (target is to reach the nominal
value)

In Table 5, the signal/noise ratios, which are calculated
using Eqs. 3 and 5 with the help of measured AGV
utilization rate, average number of awaiting products, and
average idle time values for each experiment, are

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Int J Adv Manuf Technol (2012) 59:433–443

Table 7 Confirmation runs and optimal setting showing results for the performance criterion
Optimal factor levels for the
Optimal factor levels for
the average utilization rate average number of waiting
products for AGV
if AGV

Optimal factor levels for the
average waiting times of
products in the input area

Level

Value

Level

Value

Level

Value

Speed of AGV
Capacity of AGV

1
1

3
1

2
2

7
3

2
2

7
3

Capacities of local stores

2

80

2

80

1

40

Expo (120)

2

Expo (180)

2

Expo (180)

The mean time between shippings from main store 1
Optimal predicted S/N ratios
Predicted values obtained from Confirmation runs

0.136
98.60%

depicted. At the end of all these calculations, the highest
signal/value ratio value refers to the best experimental
results. In other words, it refers to the experimental
results where AGV utilization rate is maximum, minimum
number of awaiting products, and idle time of the
products are minimum. For finalizing the optimization
phase, variation analysis is performed using the calculated
signal/noise ratios.
Later on, in order to clarify how these factors affect each
performance criterion, the statistical analysis was carried out.

These main-factor effects are plotted in Figs. 4, 5, and 6. Also,
Figs. 7, 8, and 9 show the two-factor interaction effects.
5.2 ANOVA results
Analysis of variance (ANOVA) was conducted to identify
significant factors in an automated storage system process.
ANOVA can be used to divide the total variation in the data
into variation resulting from main effects, interaction
effects, and error (Table 6).

6 Conclusion
The main factor levels A1–B1–C2–D1 are specified as the
factor levels increasing the average utilization rate of AGV,
while main factor levels A2–B2–C2/1–D2 are specified as
the factor levels reducing the awaiting average product
number. Moreover, main factor levels A2–B2–C1–D2 are
observed as the factor levels reducing the average idle time
of the awaiting parts. It is obviously seen that, the main
factor effects, two-factor interaction effects and the S/N
ratios supported the same optimal factor levels (Table 7).
As a result of this study, it has been proven that the Taguchi
experimental design is a method that can be used effectively in
not only product development and process improvement
studies but also in the designing of material handling–transfer
systems and performance optimization of automation tech-

137.24
0.00144

27.49
0.04817 s

nologies, which are to be integrated into the operation. It can
be seen that the simulation-based optimization technique can
be effectively used in a feasibility study as an experimental
tool with the advantages of cost reduction and the property of
time compression also.

References
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DOI 10.1007/s00170-011-3519-8

ORIGINAL ARTICLE

Modeling, measurement, and evaluation of spindle radial
errors in a miniaturized machine tool
S. Denis Ashok & G. L. Samuel

Received: 5 July 2010 / Accepted: 4 July 2011 / Published online: 22 July 2011
# Springer-Verlag London Limited 2011

Abstract Miniaturized machine tools have been established
as a promising technology for machining the miniature
components in wider range of materials. Spindle of a
miniaturized machine tool needs to provide extremely high
rotational speed, while maintaining the accuracy. In this work,
a capacitive sensor-based measurement technique is followed
for assessing radial errors of a miniaturized machine tool
spindle. Accuracy of spindle error measurement is affected by
inherent error sources such as sensor offset, thermal drift of
spindle, centering error, and form error of the target surface
installed in the spindle. In the present work, a model-based
curve-fitting method is proposed for accurate interpretation
and analysis of spindle error measurement data in time
domain. Experimental results of the proposed method are
presented and compared with the commonly followed discrete

Fourier transform-based frequency domain-filtering method.
Proposed method provides higher resolution for the estimation of fundamental frequency of spindle error data. Synchronous and asynchronous radial error values are evaluated in
accordance with ANSI/ASME B89.3.4M [9] standard at
various spindle speeds and number of spindle revolutions. It
is found that the spindle speed and number of spindle
revolutions does not have much influence on synchronous
radial error of the spindle. On the other hand, asynchronous
radial error motion exhibits a significant speed-dependant
behavior with respect to the number of spindle revolutions.
Keywords Spindle radial errors . Modeling . Curve fitting .
Analysis
S. D. Ashok : G. L. Samuel (*)
Manufacturing Engineering Section, Department of Mechanical
Engineering, Indian Institute of Technology Madras,
Chennai 600-036, India
e-mail:

Nomenclature
Symbol Description
Ch
Magnitude of harmonic components of spindle
error measurement data
D
Basis matrix containing the mathematical
functions of the proposed model
Ej
Sum of squared residual value for the given
discrete frequency fj
f0
Fundamental frequency of spindle error

measurement data
fj
Discrete frequency value around the rotation
frequency of spindle
H
Harmonic cutoff value for spindle radial error
measurement
X
Unknown model parameters for the given
discrete frequency fj
ah,bh
Fourier coefficients of the spindle error
measurement data
mi
Estimated value using the mathematical
model
Radial error measurement data at the given
mi'
sampling time
mci
Contribution of centering error of artifact at the
given sampling time
msi
Contribution of synchronous components at the
given sampling time
Contribution of sensor offset and thermal drift at
mti
the given sampling time
p0
Contribution of sensor offset

p 1, p 2
Coefficients of second order polynomial
function
Sampling time for the spindle error
ti
measurement data
ei
Residual of the estimated values or asynchronous
radial error