A review of soft computing techniques in materials engineering

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 10, October (2014), pp. 134-150 © IAEME
134
A REVIEW OF SOFT COMPUTING TECHNIQUES IN
MATERIALS ENGINEERING
Asif Sha. A1
, Dr. T.V. Sreerama Reddy2
, Shaleena Manafuddin3
, Dr. T. Sorna Kumar4
1
(Part-Time Ph.D Scholar, Department of Mechanical Engineering, Karpagam University,
Coimbatore-641 021, Tamilnadu, India.
Assistant Professor, IHRD College of Engineering Kollam-691531)
2
(Professor, Department of Mechanical Engineering, Bangalore Institute of Technology,
Bangalore-4, Karnataka, India)
3
(Assistant Professor, Electrical and Electronics Engineering, TKM College of Engineering,
Kollam- 691005, Kerala, India)
4
(Professor, Department of Mechanical Engineering, Thagarajar College of Engineering, Madurai,
Tamilnadu, India)
ABSTRACT
Within the last three decades, a solid and real amount of research efforts has been directed at
the application of soft computing (SC) techniques in engineering. This paper provides a systematic
review of the literature originating from these efforts which focus on materials engineering (ME)
particularly sheet metals. The primary aim is to provide background information, motivation for
application and an exposition to the methodologies employed in the development of soft computing
technologies in engineering. Our review shows that all the works on the application of SC to sheet
metal have reported excellent, good, positive or at least encouraging results. Our appraisal of the
literature suggest that the interface between material engineering and intellectual systems
engineering techniques, such as soft computing, is still blur. The need to formalize the computational
and intelligent system engineering methodology used in sheet material, therefore, arises. We also
provide a brief exposition to our on-going efforts in this direction. Although our study focuses on
materials engineering in particular, we think that our findings applies to other areas of engineering as
well.
Keywords: Soft Computing, Sheet Metal, Neural Network, Materials, Genetic Algorithm.
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1. INTRODUCTION
Materials engineering encompasses the science and art involved in the conceptualisation,
specification, design, analysis, fabrication and evaluation of generic materials in their various forms
and in different operating conditions with the aim of developing materials for an application [16].
Among them, sheet-metal working processes have been related with mankind since the Iron Age,
when human beings first discovered that metals, especially gold and silver, can be shaped in the cold
state by repetitive hammering to form thin sheets for making bowls, plates, containers, decorative
items, etc [2]. Sheet-metal forming is one of the most widely used manufacturing processes for the
fabrication of a broad range of sheet metal products in many industries such as automotive,
appliance, aerospace, and others [1] [3] [5] [6] [10] [11]. The sheet metal forming has gained a lot of
attention in modern technology because of ease with which metal may be formed into useful shapes
by plastic deformation processes in which the volume and mass of the metal are preserved and metal
is displaced from one place to another [3]. Sheet metal can be easily produced by rolling mills at low
cost and the parts that are formed from sheet metal have the advantage that the material has a high
elastic modulus and high yield strength, so that they can be both stiff and have a good strength-to-
weight ratio [7].
In recent years, new technologies are expected to respond to new industrial demands, which
are mostly seeking for precise and accurate information concerning parts design and formability of
metal sheet [4] [15]. While in sheet metal designing process, some uncertainties are caused due to
uncontrollable conditions such as metal suppliers, forming conditions, and numerical errors which
need to be taken into consideration in the design process [5].Throughout the years, technological
advances have allowed the production of extremely complex parts [6]. However, in the case of
complicated sheet metal deformation, improper design of process parameters may lead to defects.
Thus it is necessary to select the most appropriate process conditions. However, it is still a very
difficult problem to obtain the optimum result [10].
Sheet metal forming simulation plays an indispensable role in integrating manufacturing
necessities into the product design process at an early stage. In conjunction with concurrent
engineering, sheet metal forming simulation is proving to be an important tool in linking design and
manufacturing [4]. The numerical simulation of sheet metal forming processes is particularly
attractive to reduce the waste of time and cost because of the process modelling for computer
simulation by a virtual trial and error process [8]. As a result, rapid tooling technologies have made
inroads into conventional die fabrication methods with the aim of reducing the lead time and
investment costs of tooling development. One category of rapid tooling technology involves the
application of advanced polymers and composite materials to fabricate sheet metal forming dies [6].
However, there are many variables which are unknown but which can control the forming processes
in order to be able to solve the industrial problems [8]. Optimization theory provides an effective
way for further studying the relations among the sheet metal forming quality influence factors and
scientifically controlling them [9]. However soft computing based techniques and methods are
becoming more popular as they are gaining prominence in various areas of engineering. In sheet
material, SC techniques have been successfully applied in material design, improvement and
selection as well as the control of the processes for materials fabrication. This paper has three main
objectives, which are to:
- Provide a short background to the SC methods that are relevant to laminated sheet metal.
- Provide a review of the state of the art in the application of these methods in sheet metal.
- Present a discussion on our proposed framework within which the SC methods could be more
productivity deployed in sheet metal.

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2. NEED OF OPTIMIZATION FOR SHEET METAL
The metal forming process is a complex operation requiring a simple geometry to be
transformed into complex one. The main goal of optimization in metal forming is to produce sound
products through optimal process design, since the process material and die variables significantly
influence the process. In recent years, new technologies are developed to meet the industrial
requirements of sheet metal. These demands, mostly seek saving the materials cost and fulfill the
requirements such as high stiffness/weight ratio, vibration damping and special thermal and electrical
behavior. Nowadays in sheet metal industries bi-layer clad and three-layer metal/polymer/metal
laminated sheets are increasingly applied due to their numerous advantages. However, while
manufacturing the laminated sheet metal products, care must be taken for the proportion of materials
that are involved in the process. If the materials are utilized with optimal proportion, the resultant
sheet metal product will have good mechanical, electrical and thermal properties. Classical
approaches such as trial and error are tedious, ill- structured, time consuming and costly. Dynamic
programming can handle continues and discrete variables, but is limited since the process variables
involves large amount of process with wide range of values that may be active in the optimization
problem. Also, derivative based approaches are not suitable since the objective function may possess
multiple stationary points. Several authors have shown that the SC based approaches can be used to
deal with these complex real world problems.
3. SOFT COMPUTING TECHNIQUES
The term soft computing (SC) encompasses many techniques which includes: Artificial
Neural Networks (ANN), Genetic Algorithm (GA) or Evolutionary Computing (EC), Fire Fly (FF)
Algorithm (developed by Xin-She Yang in 2009), Cuckoo Search (CS)Algorithm (developed by
Xin-She Yang and Suash Deb in 2009), and part of Learning theory(LT). SC techniques are different
from analytical approach that employ computing techniques that are capable of representing
imprecise, uncertain and vague concepts. Analytical, also called hard computing, approaches on the
other hand use binary logic, crisp classification and deterministic reasoning. Techniques in SC are
able to handle non-linearity and they offer computational simplicity when compared with the
analytical methods. These techniques have been shown to be able to manage the large amount of
information and mimic biological system in learning; linguistic conceptualization, optimization and
generalization abilities. Soft computing techniques are finding growing acceptance in the material
engineering as well as laminated sheet metal optimization process. The majority of the work cited in
this review paper is the journal articles. The reason for this is that we want to report on soft
computing applications that are established in mechanical engineering.
4. ARTIFICIAL NEURAL NETWORK (ANN)
Artificial neural network represents a non-algorithmic, black box computational strategy. It is
composed of interconnected artificial neurons (Wang, 1997); each has an input/output (I/O)
characteristic and implements a local computation. Figure 1 shows an artificial neuron model with ‘r’
number of inputs. A weight W is assigned to each input U to describe its influence (strength). The
sum of the weighted inputs and the bias b forms the input to the activation function f, which can be
either linear or non-linear differentiable. The output ‘a’ from the neuron is then given by
a= f [ 1,i i
r
i=1
w u + b∑ ] ………………. (1)

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u1 w1,1
u2
: n f a
:
w1,r
ur
b
Input General Neural
Figure 1: Model of a ANN
Neural network can be described as a machine learning technique which modifies the
numerical values of its connection weights and biases through certain training algorithm which
causes the network to approach the solution of a system model. Numerous researchers under
different constraints have shown multilayer feed forward (FF) ANN capable of approximating any
finite function to any degree of accuracy [11]. Also the multilayer feed forward (FF) [11] network
structure as is so far one of the most popular and effective ANN structure The learning ability of a
neural network depends on the arbitrary choice of its architecture as well as the training algorithm.
The choice of activation ( f ) may significantly influence the applicability of the training algorithm.
Lack of success in application is likely attributable to faulty training, faulty architecture or lack of
functional relationship between inputs and outputs. One of the biggest shortcomings of FF network is
the limited availability of suitable training algorithms. So far back propagation (BP) [12] has been
found highly successful. The standard BP algorithm is a gradient descent algorithm, which adopts an
error correction based learning procedure. The main objective in ANN design and training is to
produce network that are able to apply correctly to new unforeseen inputs .By partitioning the
available data sets some testing sets are reserved for accessing the generalization performance. This
is known as cross validation. Over-training might lead to memorization, and therefore pure
performance when applying the testing sets. ANN with one hidden layer has been found to be
effective for most practical application of material engineering.
4.1 APPLICATIONS OF THE ANN TECHNIQUE
As shown in Figure 2 the application of ANN in materials engineering is increasing in
popularity from only about 3 papers in 1995 to more than 75 papers reporting the application of
ANN in 2014. In most of the papers we reviewed the process of developing an ANN based model
consists of the following stages.
- Generation of training data.
- Selection of a network type.
- Selection of the input and the output for the network.
- Design of a suitable network configuration.
- Selection of a suitable training strategy.
- Training and validation of the resulting network
As shown in Figure 3 ANN based tools have been applied in prediction, modeling, control,
identification, design and optimization areas of the material engineering. The majority of the
∑ f

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976
6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 10, October (2014
application we reviewed, about 74
closely followed by materials properties modeling
properties optimization and design
material process control represents 4
use the MATLAB software for the
number of input to the ANN is very large.
analysis the model behavior and
five; the model should be modularized for effective analysis. Some work
with more than two hidden layers. The practicability of such model in the material engineering
application in terms of economy of tool
Figure 2: Graph of
Figure
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International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976
6499(Online) Volume 5, Issue 10, October (2014), pp. 1
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about 74%, has been in the area of materials proportion prediction. This is
closely followed by materials properties modeling, about 54%. Works in the areas of materials
properties optimization and design account for 7 and 6%, respectively, and model identification and
represents 4% each of the reviewed work. The majority of the work reviews
the MATLAB software for the implementation of their models. For example, in some work th
to the ANN is very large. The choice of ANN design makes it
the model behavior and explain its operation. If the input to an ANN model is more than
the model should be modularized for effective analysis. Some work used ANN architecture
ion in terms of economy of tool
Graph of ANN - Number of publications by year
Figure 3: Application of ANN in ME
2000 2005 2010
Year of publication
Optimization Modelling Analysis Identification
Type of Application
34-150 © IAEME
%, has been in the area of materials proportion prediction. This is
%. Works in the areas of materials
and 6%, respectively, and model identification and
majority of the work reviews
s. For example, in some work the
choice of ANN design makes it difficult to adequately
If the input to an ANN model is more than
used ANN architecture
mber of publications by year
2010 2015
Identification Design

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is difficult to justify as it is well known that ANN with one layer will produce a good approximation.
In most of the work we reviewed, however ANN model have been shown to generate better
prediction than the classical linear regression. ANN has a number of limitations in material
engineering. First, there is the need to manage a large number of parameters used for controlling
variable in ANN model. This process is not systematic but intuitive. Inability to appropriately
manage ANN model parameters accounts for the difficulty in obtaining stable solutions and danger
of over-fitting resulting in the lack of generalization capability in addition. ANN results a “black
box” model which is not very useful in situation that is important to understand the operation of a
system, such as the design of materials meant for use in safety critical system.
5. GENETIC ALGORITHM
The principle of Genetic Algorithm and the mathematical framework underlying it were
developed in the late 1960’s (Holland, 1962; Kristinson and Dumont, 1992; Koppen et al., 2006).
GA is normally discussed in the context of Evolutionary Computing (EC). The method of EC is
Genetic Algorithm. In GA, attempt is made to model the processes underlying population generic
theory by using random search. GA uses the survival of fittest strategy, where stronger individuals in
the population have a higher chance of creating an offspring. To achieve this, the current input
(population) is used to create a new and better population based on specified constraints. The inputs
that are normally represented as string can model chromosome in human genetics. In material
engineering, for example, the input strings will represent some properties of materials that are of
interest. The success of GA application in material engineering task is dependent on the encoding of
variables that describe material attributes in the form of strings. The number and type of variables
that is to be encoded as a string depends upon the resolution of data and scale of the problem. Each
input variable can be viewed as a gene in the chromosome that represents the input space. During the
mating process, the strings that describe material properties are selected and paired. This pair is
called the parent string. In the basic cross over operator, two new strings called offspring are created
for the current generation which is frequently used in materials engineering. In the final step called
mutation, it is conducted to prevent the premature convergence of the design variables. The GA
process continues until a set of stop criteria are met. Such stop criteria may be when an individual
recognizes all the examples or when a maximum number of generations have been run; in materials
engineering such criteria may correspond to specified material yield strength or hardness of the
composite.
5.1 APPLICATION OF GENETIC ALGORITHM
As shown in the Figure 4, the application of GA is increasing; there are about 4 papers in
2004 which increased to 68 papers in 2014. The majority of the papers in 2014 appear in the special
issue of Computational Materials Science Journal. As shown in the Figure 5, the GA’s technique has
been applied in the areas of materials properties modeling, optimization, identification, prediction
and design. The majority of the application is in material properties

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Figure 4: Graph of GA-Number of publications by year
Figure 5: Application of GA in ME
modeling and optimization which account for about 45% each of the total paper reviewed. Model
identification accounts for 20% while the material properties prediction accounts for 6%. From all of
the paper reviewed it was shown that GA’s have proven effective in the materials properties
optimization problems and the areas that require parameter training such as function optimization,
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materials processing and system identification. The limitation of GA‘s in the material engineering
are of the two reasons. The first is the use of randomness in obtaining optimal solution. From the
engineering point of view, the concept of randomness is difficult to explain and justify in real-life
application, particularly in safe critical systems. The second limitation relates to the intuitiveness of
evolution theory in problem solving. How the theory of human genetic factors into human problem
solving is not very clear.
6. FIREFLY ALGORITHM
Firefly Algorithm (FA) was developed by Xin-She Yangin late 2007 and 2008 at Cambridge
University, which was based on the flashing patterns and behavior of fireflies. In essence, FA uses
the following three idealized rules:
- All fireflies are unisexual that is one firefly will be attracted by all others
- Attraction is dependent on the amount of brightness that is a less bright firefly is attracted to a
brighter one.
- The brightness of the firefly is equivalent to the objective function.
The attractiveness is dependent on the distance between the two fireflies as the intensity of
light decreases as the distance between the two fireflies’ increases. Therefore, the closer the fireflies,
the more attractive they seem to each other. The attractiveness of the fireflies varies with the
brightness which is in turn related to the objective function in the mathematical domain. The
intensity decreases with the increases in distance, and hence, a given firefly will be attracted to a
firefly that is close to it even though it is less bright than a farther but brighter firefly. The intensity
of light is known to vary inversely with the square of increasing distance or radius given by:
I(r) α
૚
࢘૛
Where I(r) represent light intensity as a function of distance and r is the radius. This can be
converted to equality by adding a constant I(s), which is intensity of the source. The intensity of light
in the real situation also depends upon atmospheric factors called the absorption coefficient, and
therefore, the inclusion of the absorption coefficient (γ) changes the equation to:
I(r) = I0 ࢋିࢽ࢘૛
Attractiveness of the firefly is dependent and is directly proportional to the intensity of light.
Hence the intensity equation can be transformed to represent the attractiveness as follows:
β=
ࢼ૙
૚ାࢽ࢘૛
Here β represents the attractiveness of the firefly and β0 represents the attractiveness at a
radius zero, the attractiveness can vary as any power of radius rather than square root. The mapping
of the parameters and the corresponding notation used in the algorithm is shown in Table 1.

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Table 1: Parameters and Definitions
The optimization process depends on the brightness of the fireflies and the movement of the
fireflies towards their brighter counterparts. Every firefly is attracted towards the other, depending
upon brightness because the fireflies are all unisexual according to the first assumption. The Figure 6
shows the flowchart illustrating firefly algorithm. This algorithm starts by initializing a population of
fireflies and each firefly is different from the other in the swarm. The differentiation is based on the
brightness of the firefly. During the iterative process, the brightness of one firefly is compared with
the others in the swarm and the difference is the brightness that triggers the movement. The distance
travelled depends on the attractiveness of the fireflies. During the iterative process, the best solution
thus far is continuously updated and the process goes on until certain stopping conditions are
satisfied. After the iterative process comes to a halt, the best solution of the evaluation is determined
and the post process is initialized to obtain the result.
6.1. APPLICATION OF FIREFLY ALGORITHM
Firefly algorithm has attracted much attention and has been applied to many applications.
The application of firefly in material engineering is increasing today. There are about two papers in
2009 and was increased to nine papers reporting the application of firefly in 2014 related to material
engineering, which was shown in the Figure 7. As shown in Figure 8, firefly has been applied in the
area of material properties modeling, selection, prediction, evaluation, design, clustering,
optimization, control and model identification. The material properties optimization by firefly is
about 18% that is to be reviewed. This is followed closely by material properties prediction which
accounts for 15% of the paper reviewed. Amazingly firefly algorithm can have some significant
advantages over other meta-heuristics such as GA. Two of such advantages are automatic
subgrouping and ability to deal with multi model problems. Firefly can automatically sub-divide into
sub groups and each group can potentially swarm around a local optimum and all optima can be
obtained simultaneously if the number of fireflies is much higher than the number of modes. Thus
firefly algorithm can handle multi model problems very efficiently due to this sub grouping ability.
The other advantage is that the firefly algorithm does not use velocity. Therefore firefly algorithm is
much simpler to implement. The firefly algorithm has been proved to be efficient at solving
optimization tasks and can be more efficient than other meta-heuristic algorithms when applied to
continuous constrained optimization task, stochastic functions and multi-model functions.
7. CUCKOO SEARCH ALGORITHM
Cuckoo Search (CS) Algorithm was developed by Xin-She Yang and Suash Deb in 2009
which was based on the obligate brood parasitic behavior of some cuckoo species in combination
with the Levy flight behavior of some birds and fruitflies. In essence, CS uses the following three
idealized rules:
Sl.No Parameter Notation in Algorithm
1 Brightness Objective function
2 Beta (β) Attractiveness
3 Alpha (α) Randomization Parameter
4 Gamma (γ) Absorption Coefficient
5 Number of generation Iteration
6 Number of fireflies Population
7 Dimension Problem Definition
8 r Radius, Time interval etc. (depends on application)

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- Each cuckoo lays one egg at a time, and dump its egg in randomly chosen nest.
- The best nests with high quality of eggs will carry to the next generation.
- The number of available host nests is fixed, and the egg laid by a cuckoo is discovered by the
host birth, a probability pa ϵ [0,1].
No
Yes
Figure 6: The flowchart of Firefly algorithm
Start
Generate initial population of
Fireflies
Evaluate fitness of all fireflies from
the objective functions
Update the light intensity
(Fitness value) of fireflies
Rank the fireflies and
update the position
Reach maximum
iteration
Optimal result

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Figure 7: Graph of FA - Number of publications by year
Figure 8: Application of FA in ME
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The CS is a population based optimization algorithm and similar to many others meta
heuristic algorithm start with a random initial population which is taken as host nests or eggs. The
CS algorithm essentially works with three components selection of the best by keeping the best nest
of solutions; replacement of the host eggs with respect to the quality of the new solution or cuckoo
eggs produced based on randomization via Levy flights globally and discovery of some cuckoo eggs
by the host birds and by replacing according to the quality of the local random walks. Each egg in
the nest represents solution, and cuckoo egg represents new solution. The aim is to use the new and
potentially better solution (cuckoos) to replace not-so-good or inferior solution in the nest. In the
simplest form, each nest has one egg. The algorithm can be extended to more complicated cases in
which each nest has multiple eggs representing a set of solutions. Cuckoo search algorithm is the
fastest to converge to its optimal solution. When compared with genetic algorithm, Cuckoo search
algorithm is very efficient and proves to be superior for all the test problems. This is partly due to the
fact that there are fewer parameters to be fine-tuned in Cuckoo search than in genetic algorithms and
swarm optimization. The selection of the best by keeping the best nest or solutions is equivalent to
some forms of elitism commonly used in GA, which ensures the best solution is passed on to nest
iteration and there is no risk. The best solutions are cast out of the population. The Figure.9 shows
the flowchart illustrating Cuckoo search algorithm.
7.1. APPLICATION OF CUCKOO SEARCH ALGORITHM
Cuckoo Search Algorithm (CS) has been applied as optimization for various tasks including
finding optimal features; optimize the parameters of various classifiers including Neural Network,
job scheduling, structural design optimization of a vehicle component etc. A quick Google search, at
the time of writing this review on 2014 lead to about 265 papers on Cuckoo Search from 2009. They
certainly form active research topics in optimization and computational intelligence. But in the
materials engineering, the cuckoo search algorithms is a newer technic in the optimization and
design process and are efficient with the other algorithm. Since it was a newer technique in the
materials engineering, it was implemented in the cases of materials engineering for the last years. In
order to meet today's vehicle design requirements and to improve the cost and fuel efficiency, there
is an increasing interest to design light-weight and cost-effective vehicle components. In this
research, a new optimization algorithm, called the Cuckoo Search Algorithm (CS), is introduced for
solving structural design optimization problems. The CS algorithm is applied to the structural design
optimization of a vehicle component to illustrate how the present approach can be applied for solving
structural design problems. Results show the ability of the CS to find better optimal structural design.
Moreover in the engineering application field except mechanical engineering plays an important role
for the optimization. A comparative study of soft computing techniques features is also included in
Table 2. Moreover we think that due to this higher performance the engineers in materials
engineering use these soft computing techniques for the application such as modeling, selection,
prediction, evaluation, design, clustering, optimization, control and model identification.

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No
Yes
No
Yes
Figure 9: The flowchart of Cuckoo search algorithm
Start
Initializes cuckoos with eggs
Lay eggs in different nests
Some of eggs were detected
and kill
Move all cuckoos toward
best environment
Determine cuckoos
societies
Determine egg laying radius
for each cuckoo
Find nests with best survival
rate
Let’s eggs grow
Check survival of eggs in nest
Kill cuckoos
in worst area
End
Population is less than
max value
Stop condition
satisfied

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Table 1: Comparative study of soft computing techniques features
VH- very high, H- high, M- medium, L- low, VL- very low
8. FORMALISING THE SOFT COMPUTING APPLICATION METHODOLOGY
Soft computing techniques provide appealing alternatives for supporting the materials
engineering process. Although the soft computing constituents have several advantages when used
individually, a synergistic integration of these complementary techniques into hybrid models have
the potential for the development of practical and efficient intelligent materials engineering tools.
However the application of SC in material engineering is evolving. Our review of literature revealed
that different researchers are employing different view of concepts as well as varying
implementation approach. This makes it difficult to access in a definite manner, the overall
implications or outcome of a given implementation. There is no doubt in the fact that materials are
potentially life critical due to their pervasiveness. Qualities of engineering materials are crucial to the
performance of modern safety critical systems and a number of materials related failures have been
recorded in recent times. During the development of new materials, there is the possibility of gaps
between material requirement and the engineer’s concept of those requirements. This will result in
the generation of compromised specification and hence an unrealistic application of SC solution.
Some form of standardization then becomes crucial inorder to achieve manageable and acceptable
Engineering practice. In our ongoing research we are introducing advanced nature inspired algorithm
in the material engineering- sheet metal for the optimization purpose. The fundamentals of this
specification process are similar to the principles used in modern software engineering. The Figure
10 gives an overview of the proposed framework.
9. CONCLUSION
Modern materials Engineering task involves the development of products presenting design
challenges that involves complex situations with overwhelming data and information which are
further constrained by confounding materials processing and fabrication decision This complexity
seems to have motivated the recent cross fertilization of ideas between areas of
Sl.
No.
Methods Learning
capacity
Knowledge
representation
capacity
Real-Time
operation
functionality
Optimization
capacity
Data
requirements
Expert
Input
level
1 ANN VH H H M VH VL
2 GA M M H VH M M
3 FA VH H H VH M M
4 CS VH H H VH H M

148
No
Yes
No
Yes
Figure 10: Overview of the proposed framework.
Functional
Specifications
Non-Functional
Specifications
Formal
Specifications
Materials design
Selection of soft computing model
Simulation and Experimentation
Fabrication data generation
Result satisfactory?
Design
error?
Materials
properties
database
Statement of materials requirements

149
research: such as materials engineering, computer engineering, intelligent systems engineering and
Engineering Physics. All researchers that have used SC based approach in materials engineering
have reported excellent, good, positive or at least encouraging results. The lack of negative results
might be partially due to the fact that materials engineering problems are simplified to manageable
and predictable applications. The tool of the trade is also changing from the traditional mathematical
and analytical approaches to modeling, simulation and computational approaches. The interface
between materials engineering and intelligent system engineering techniques, such as the soft
computing is still blur. There is, therefore, the need to put in place some formal structure that remove
or reduce grey areas. As computer is becoming an indispensable tool in materials engineering it
becomes, desirable to have a computational framework within which various materials could be
explored from conceptualization, to design through evaluation to fabrication using the computer. Our
computational approach to materials engineering has the potential of making material engineering
process more effective and efficient.
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[3] AmitGoel, “Blank optimization in sheet metal forming using finite element simulation”
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[4] Hakim S. Sultan Aljibori and Abdel MagidHamouda, “Finite Element Analysis of Sheet
Metal Forming Process”, European Journal of Scientific Research, Vol.33, No.1, pp.57-69,
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