full report final project btech

PROJECT-II REPORT
on
OPTIMIZATION OF WEDM PROCESS PARAMETERS
USING TAGUCHI METHOD AND GREY RELATIONAL
ANALYSIS
Submitted in partial fulfillment of the requirement
for the award of the degree
of
Bachelor of Technology
in
Mechanical Engineering
By
LALIT (2910406) VAIBHAV (2910417)
NIGAM (2910430) ANISH (2910437)
Semester: 8th
Under the Supervision of
Er. DEEPAK MITTAL
(Assistant Professor)
Department of Mechanical Engineering
Kurukshetra Institute of Technology & Management, Kurukshetra
Kurukshetra University, (Haryana)
APRIL, 2014

Student’s Declaration
We Lalit (2910406), Vaibhav (2910417), Nigam (2910430) and Anish (2910437) the
students of Department of Mechanical Engineering, Kurukshetra Institute of
Technology & Management, Kurukshetra hereby declare that we have worked on the
Project-I entitled “OPTIMIZATION OF WEDM PROCESS PARAMETERS
USING TAGUCHI METHOD AND GREY RELATIONAL ANALYSIS” and have
prepared the project report ourselves in partial fulfillment of the requirement for the
award of the degree of Bachelor of Technology in Mechanical Engineering from
Kurukshetra University, Kurukshetra.
Date: Lalit (2910406)
Vaibhav (2910417)
Nigam (2910430)
Anish (2910437)

Certificate
This is to certify that Mr. Lalit (2910406), Vaibhav (2910417), Nigam (2910430) and
Anish (2910437) the students of Department of Mechanical Engineering, Kurukshetra
Institute of Technology & Management, Kurukshetra have worked on the project
entitled “OPTIMIZATION OF WEDM PROCESS PARAMETERS USING
TAGUCHI METHOD AND GREY RELATIONAL ANALYSIS” under my
supervision during the period from January, 2014 to April, 2014 and have completed
the Project-II in partial fulfillment of the requirement for the award of the degree of
Bachelor of Technology in Mechanical Engineering from Kurukshetra University,
Kurukshetra.
Approved as to style and content by:
Er. Deepak Mittal
Assistant Professor
Countersigned By:
Er. Viraj Tyagi
Associate Professor & Head,

Acknowledgement
Words are inadequate and out of place at times particularly in context of expressing
sincere feeling in the contribution of this work, is no more than a mere ritual. It is our
privilege to acknowledge with respect & gratitude, the keen valuable and ever-available
guidance rendered to us by Er.Deepak Mittal Without his counsel and guidance, it
would have been impossible to complete the project in this manner.
We shall always be highly grateful to Dr. P.J George, Director-Principal, Kurukshetra
Institute of Technology and Management, Kurukshetra, for providing this opportunity
to carry out the present work.
The guidance and encouragement received from Er. Viraj Tyagi, Associate professor
and Head of Department of Mechanical Engineering has been of great help in carrying
out the present work and is acknowledged with reverential thanks. We express gratitude
to other faculty members of Mechanical Engineering Department, Kurukshetra Institute
of Technology and Management, Kurukshetra for their intellectual support through the
course of this work.
Finally, we are indebted to our family and for their ever available help in
accomplishing this task successfully. Above all we are thankful to the almighty god for
giving strength to carry out the present work.
Lalit (2910406)
Vaibhav (2910417)
Nigam (2910430)
Anish (2910437)

CONTENTS
Sr. No. Description Page No.
Student’s Declaration i
Certificate ii
Acknowledgement iii
List of Figures iv
List of Tables v
1. Chapter 1: Introduction 1-15
1.1 Introduction of WEDM 1
1.2 Importance of WEDM process in present day 2
manufacturing
1.3 Basic Principle of WEDM Process 3
1.4 Mechanism of Material Removal in WEDM Process 5
1.5 WEDM Parameters 6
1.6 Dielectric Fluid 7
1.7 Design Variable 8
1.8 Workpiece Material 8
1.9 Introduction to Taguchi Method 8
1.10 Steps in Taguchi Methodology 8
1.11 Introduction to Grey based Taguchi Method 9
1.12 Advantages of WEDM Process 14
1.13 Disadvantages of WEDM Process 15
1.14 Applications of WEDM Process 15
1.15 Motivation 15
2. Chapter 2: Literature Survey 16-25
2.1 Literature Survey 20
2.2 Formulation of Problem 25
3. Chapter 3: Work Undertaken 26-34
3.1 Machine Tool 26
3.2 Preparation of Specimens 27
3.3 Experimentation 28
3.4 Working Mechanism 28

3.5 Fixed Parameters 29
3.6 Parameters and Taguchi Level 29
3.7 Experimental details 30
3.8 Cost Estimation 34
4. Chapter 4: Results and Discussion 35-52
4.1 Optimization of MRR 35
4.2 Optimization of Surface Roughness 37
4.3 Optimization of Electrode Consume 40
4.4 Optimization of MRR, Electrode consume and 43
Surface Roughness (Grey Analysis)
4.5 Discussion of Result 49
4.6 Confirmation test 51
5. Chapter 5: Conclusion and Future Scope 53-54
5.1 Conclusion 53
5.2 Future Scope 53
References 55
Appendix –I 58

List of Figures
Figure No. Description Page no.
Fig. 1.1 Schematic Diagram of the Basic Principle of WEDM 4
Process
Fig. 1.2 Block Diagram of Wire-EDM 4
Fig. 1.3 Detail of WEDM Cutting Gap 5
Fig. 1.4 Procedure of the grey-based Taguchi method 10
Fig. 1.5 Relationship between distinguishing coefficient and 13
grey relational co-efficient.
Fig. 3.1 Pictorial View of WEDM Machine Tool 27
Fig. 3.2 Plate Material Blank Mounted on WEDM Machine 27
Fig. 4.1 Effect of WEDM Parameters on M.R.R. for S/N 36
Ratio
Fig. 4.2 Effect of WEDM Parameters on M.R.R. for Means 37
Fig. 4.3 Effect of WEDM Parameters on Surface roughness 39
for S/N Ratio
Fig. 4.4 Effect of WEDM Parameters on Surface roughness 40
for mean Ratio
Fig. 4.5 Effect of WEDM Parameters on Electrode Consume 41
for S/N Ratio
Fig. 4.6 Effect of WEDM Parameters on electrode consumed 43
for mean Ratio
Fig. 4.7 Effect of WEDM Parameters on Surface roughness, 49
Electrode consume and MRR for S/N Ratio
Fig. 4.8 Effect of WEDM Parameters on Surface roughness, 50
Electrode consume and MRR for Mean

List of Tables
Table No. Description Page no.
Table 3.1 Fixed Parameters 29
Table 3.2 Response parameters and control parameters with 29
their levels
Table 3.3 Experimental Design Using L9 orthogonal array 30
Table 3.4 Metal Removal Rate (MRR) 32
Table 3.5 Electrode Consume 33
Table 3.6 Surface Roughness 33
Table 3.7 Cost Detail 34
Table 4.1 Experimental Result and Corresponding S/N Ratio 35
for MRR
for Surface Roughness
for Electrode Consume
Table 4.4 Experimental results 44
Table 4.5 Grey Relational Generation 45
Table 4.6 Grey Relational Deviation Sequence 46
Table 4.7 Grey Relational Coeficients 47
Table 4.8 Grey Relational Grade 48
Table 4.9 Optimum Value of Parameter According to 51
S/N Ratio

Chapter 1
INTRODUCTION
1.1 Introduction of WEDM
Electrical discharge machining (EDM) is a nontraditional, thermoelectric process which
erodes material from the workpiece by a series of discrete sparks between a work and
tool electrode immersed in a liquid dielectric medium. These electrical discharges melt
and vaporize minute amounts of the work material, which are then ejected and flushed
away by the dielectric. The sparks occurring at high frequency continuously &
effectively remove the work piece material by melting & evaporation. The dielectric
acts as a deionising medium between 2 electrodes and its flow evacuates the re
solidified material debris from the gap assuring optimal conditions for spark generation.
In wire edm metal is cut with a special metal wire electrode that is programmed to
travel along a preprogrammed path. A wire EDM generates spark discharges between a
small wire electrode (usually less than 0.5 mm diameter) and a workpiece with
deionized water as the dielectric medium and erodes the workpiece to produce complex
two- and three dimensional shapes according to a numerically controlled (NC) path.
The wire cut EDM uses a very thin wire 0.02 to 0.3 mm in diameter as an electrode and
machines a work piece with electrical discharge like a band saw by moving either the
work piece or wire erosion of the metal utilizing the phenomenon of spark discharge
that is the very same as in conventional EDM . The prominent feature of a moving wire
is that a complicated cutout can be easily machined without using a forming electrode
.Wire cut EDM machine basically consists of a machine proper composed of a work
piece contour movement control unit ( NC unit or copying unit), work piece mounting
table and wire driven section for accurately moving the wire at constant tension ; a
machining power supply which applies electrical energy to the wire electrode and a unit
which supplies a dielectric fluid ( distilled water) with constant specific resistance.
The main goals of WEDM manufacturers and users are to achieve a better stability and
higher productivity of the WEDM process, i.e., higher machining rate with desired
accuracy and minimum surface damage. However, due to a large number of variables

and the stochastic nature of the process, even a highly skilled operator working with a
state-of-the-art WEDM is unable to achieve the optimal performance and avoid wire
rupture and surface damage as the machining progresses. Although most of the WEDM
machines available today have some kind of process control, still selecting and
maintaining optimal settings is an extremely difficult job. The lack of machinability
data on conventional as well as advanced materials, precise gap monitoring devices,
and an adaptive control strategy that accounts for the time-variant and stochastic nature
of the process are the main obstacles toward achieving the ultimate goal of unmanned
WEDM operation.
1.2 Importance of WEDM Process in Present Day Manufacturing
Wire electrical discharge machining (WEDM) technology has grown tremendously
since it was first applied more than 30 years ago. In 1974, D.H. Dulebohn applied the
optical-line follower system to automatically control the shape of the components to be
machined by the WEDM process. By 1975, its popularity rapidly increased, as the
process and its capabilities were better understood by the industry. It was only towards
the end of the 1970s, when computer numerical control (CNC) system was initiated
into WEDM, which brought about a major evolution of the machining process (Ho et.
al., 2004).
Its broad capabilities have allowed it to encompass the production, aerospace and
automotive industries and virtually all areas of conductive material machining. This is
because WEDM provides the best alternative or sometimes the only alternative for
machining conductive, exotic, high strength and temperature resistive materials,
conductive engineering ceramics with the scope of generating intricate shapes and
profiles (Kozak et.al., 2004 and Lok and Lee, 1997).
WEDM has tremendous potential in its applicability in the present day metal cutting
industry for achieving a considerable dimensional accuracy, surface finish and contour
generation features of products or parts. Moreover, the cost of wire contributes only
10% of operating cost of WEDM process. The difficulties encountered in the die
sinking EDM are avoided by WEDM, because complex design tool is replaced by
moving conductive wire and relative movement of wire guides.

1.3 Basic Principle of WEDM Process
The WEDM machine tool comprises of a main worktable (X-Y) on which the work
piece is clamped; an auxiliary table (U-V) and wire drive mechanism. The main table
moves along X and Y-axis and it is driven by the D.C servo motors. The travelling wire
is continuously fed from wire feed spool and collected on take up spool which moves
though the work piece and is supported under tension between a pair of wire guides
located at the opposite sides of the work piece. The lower wire guide is stationary
where as the upper wire guide, supported by the U-V table, can be displaced
transversely along U and V-axis with respect to lower wire guide. The upper wire guide
can also be positioned vertically along Z-axis by moving the quill.
A series of electrical pulses generated by the pulse generator unit is applied between the
work piece and the travelling wire electrode, to cause the electro erosion of the work
piece material. As the process proceeds, the X-Y controller displaces the worktable
carrying the work piece transversely along a predetermined path programmed in the
controller. While the machining operation is continuous, the machining zone is
continuously flushed with water passing through the nozzle on both sides of work
piece. Since water is used as a dielectric medium, it is very important that water does
not ionize. Therefore, in order to 3prevent the ionization of water, an ion exchange
resin is used in the dielectric distribution system to maintain the conductivity of water.
In order to produce taper machining, the wire electrode has to be tilted. This is achieved
by displacing the upper wire guide (along U-V axis) with respect to the lower wire
guide. The desired taper angle is achieved by simultaneous control of the movement of
X-Y table and U-V table along their respective predetermined paths stored in the
controller. The path information of X-Y table and U-V table is given to the controller in
terms of linear and circular elements via NC program. Figure 1.1 exhibits the schematic
diagram of the basic principle of WEDM process (Saha et. al., 2004). The complete
block diagram of WEDM is shown in Figure1.2. Figure 1.3 shows the detail of WEDM
cutting gap.

Fig 1.1: Schematic Diagram of the Basic Principle of WEDM Process
Fig 1.2: Block Diagram of Wire-EDM

Fig 1.3: Detail of WEDM Cutting Gap
1.4 Mechanism of Material Removal in WEDM Process
The mechanism of metal removal in wire electrical discharge machining mainly
involves the removal of material due to melting and vaporization caused by the electric
spark discharge generated by a pulsating direct current power supply between the
electrodes. In WEDM, negative electrode is a continuously moving wire and the
positive electrode is the work piece. The sparks will generate between two closely
spaced electrodes under the influence of dielectric liquid. Water is used as dielectric in
WEDM, because of its low viscosity and rapid cooling rate (Lok and Lee, 1997).
No conclusive theory has been established for the complex machining process.
However, empirical evidence suggests that the applied voltage creates an ionized
channel between the nearest points of the work piece and the wire electrodes in the
initial stage. In the next stage the actual discharge takes place with heavy flow of
current and the resistance of the ionized channel gradually decreases. The high intensity
of current continues to further ionize the channel and a powerful magnetic field is
generated. This magnetic field compresses the ionized channel and results in localized
heating. Even with sparks of very short duration, the temperature of electrodes can
locally rise to very high value which is more than the melting point of the work
material due to transformation of the kinetic energy of electrons into heat. The high
energy density erodes a part of material from both the wire and work piece by locally
melting and vaporizing and thus it is the dominant thermal erosion process.

1.5 WEDM Parameters
 Spark On-time (pulse time or Ton):
It is the duration of the time (μs) that current is allowed to flow per cycle. MRR
varies directly proportional to the amount of energy applied during this on-time.
This energy is really controlled by the peak current and length of the on-time.
 Spark off time (pause time or Toff):
Here, this time allows the molten material in getting solidified and to be wash out
of the arc gap. This parameter affects the speed and the stability of the cut. Thus, if
the off-time is too short, it creates an unstable spark.
 Arc gap (or gap):
The Arc gap is distance between the electrode and work piece while the process of
EDM takes place. It might be called as spark gap. Spark gap can be handled by
servo system.
 Discharge current (current Ip):
Current is measured in amp Allowed to per cycle. Discharge current directly alters
with the Material removal rate.
 Duty cycle (τ):
It is a percentage of the on-time relative to the total cycle time. This
Parameter is measured by dividing the on-time by the total cycle time (on-time
pulse off time).
τ = Ton
Ton +Toff
 Voltage (V):
It is a potential that can be measure as volt, it is also effects the material removal
rate and allowed per cycle. Voltage is given as 50 V in this experiment.

 Diameter of electrode (D):
It is the electrode of Cu-tube there are two different size of diameter 4mm and
6mm in this experiment. This tool is used as electrode and also for internal
flushing.
 Over cut :
It is a clearance per side between the electrode and the work piece after the
Machining operation.
1.6 Dielectric Fluid
In WEDM, as has been discussed earlier, material removal mainly occurs due to
melting and thermal evaporation. As thermal processing is required to be carried out in
absence of oxygen so that the process can be controlled and its oxidation is avoided.
Often oxidation leads to poor surface conductivity (electrical) of the work piece
blocking further machining. Hence, dielectric fluid should provide an oxygen free
machining environment and at the same time it should have enough strong dielectric
resistance so that electrically it does not breakdown too easily but at the same time
ionize when electrons collide with its molecule. Moreover, it should be thermally
resistant during sparking as well.
The dielectric fluid and its functions:
 It helps in initiating discharge acting as a conducting medium when ionised,
and conveys the spark. Its energy is concentrated to a very narrow region.
 (b) It helps in cooling the work, quenching the spark, tool electrode and enables
arcing to be prevented.
 (c) Eroded metal is carried away along with it.
 (d) It acts as a coolant while quenching the sparks.
The metal removal rate, electrode wear rate and other operation characteristics are also
influenced by the dielectric fluid. The general dielectric fluids used are transformer on
silicon oil, kerosene (paraffin oil), WEDM oil and de-ionized water are used as
dielectric fluid in WEDM. Tap water is not used as it gets early ionized and thus
breakdown due to presence of salts due to the occurrence of impurities. Dielectric
medium is generally passed forcing around the spark zone and also applied through the
tool to achieve efficient removal of molten material.

1.7 Design Variable
Design parameter, constant parameter and process parameter are following ones,
Design parameters are
 Material removal rate.
 2. Tool consume
 3. Surface roughness
1.8 Workpiece Material
The D-2 steel has been used as a work piece material for the present experiments.D-2
steel is steel that is vaccum heat treated i.e. raised to a high temperature and rapidly
cooled by a vaccum process. It is an ideal steel to use for the punch and die or injection
mould tools. It is a difficult material to machine and require a special wheel for surface
grinding after heat treatment. D-2 is high carbon, high chromium tool steel
manufactured for high abrasive wear applications. D-2 is used for barrel liners in the
plastic molding industry, die component in the metal stamping industry.
1.9 Introduction to Taguchi Method
Dr. Taguchi of Nippon Telephones and the Telegraph Company, Japan had developed
method based on "ORTHOGONAL ARRAY" experiments which gives us much
reduced "variance" for the experiment with "optimum settings" of control parameters.
Thus the marriage of Design of Experiments with optimization of control parameters to
obtain the BEST results is achieved in the Taguchi Method. "Orthogonal Arrays" (OA)
provide a set of well balanced (minimum) experiments and Dr.Taguchi's Signal-to-
Noise ratios(S/N), which are log functions of desired output, serve as objective
functions for optimization, help in data analysis and prediction of optimum results.
1.10 Steps in Taguchi Methodology
Taguchi proposed a standard 8-step procedure for applying his method for optimizing
any process,
Step-l: Identify the main function, side effects, and failure mode.
Step-2: Identify the noise factors, testing the conditions, and quality characteristics.

Step-3: Identify the objective function to be optimized.
Step-4: Identify the control factors and their levels.
Step-5: Select the orthogonal array matrix experiment.
Step-6: Conduct the matrix experiment.
Step-7: Analyze the data; predict the optimum levels and the performance.
Step-8: Perform the verification experiment and plan the future action.
1.11 Introduction to Grey based Taguchi Method
Genichi Taguchi, a Japanese scientist, developed a technique based on OA of
experiments. This technique has been widely used in different fields of engineering to
optimize the process parameters. The integration of DOE with parametric optimization
of process can be achieved in the Taguchi method. An OA provides a set of
well-balanced experiments, and Taguchi’s signal-to-noise. (S/N) ratios, which are
logarithmic functions of the desired output, serve as objective functions for
optimization. It helps to learn the whole parameter space with a small number
(minimum experimental runs) of experiments. OA and S/N ratios are used to study the
effects of control factors and noise factors and to determine the best quality
characteristics for particular applications. The optimal process parameters obtained
from the Taguchi method are insensitive to the variation of environmental conditions
and other noise factors. However, originally, Taguchi method was designed to optimize
single-performance characteristics. Optimization of multiple performance
characteristics is not straightforward and much more complicated than that of single-
performance characteristics. To solve the multiple performance characteristics
problems, the Taguchi method is coupled with grey relational analysis. Grey relational
analysis was first proposed by Deng in 1982 to fulfill the crucial mathematical criteria
for dealing with poor, incomplete, and uncertain system. This grey-based Taguchi
technique has been widely used indifferent fields of engineering to solve multi-
response optimization problems.

The procedure of the grey-based Taguchi method is shown in Fig. 1.In Figure 1, steps
1, 2 and 7 are general procedures of the Taguchi method and steps 3 to 6 are the
procedure of GRA.
Step 1: Experiment design and execution
Classical process parameter design is complex and not easy to use (Fisher 1925).A
large number of experiments have to be carried out when the number of process
parameters increases. To solve this problem, the Taguchi method uses a special design
of orthogonal arrays to study the entire process parameter space with only a small
number of experiments (Lin and Lin 2002). Therefore, the first step of the proposed
procedure of simulation optimization is to select an appropriate orthogonal array in
which every row represents a simulation scenario. The simulation runs are then
executed by following the experimental structure of the selected orthogonal array.
Fig 1.4: Procedure of the grey-based Taguchi method.
Step 2: Signal-to-noise ratio calculation
The Taguchi method aims to find an optimal combination of parameters that have the
smallest variance in performance. The signal-to-noise ratio (S/N ratio, η) is an effective
way to find significant parameters by evaluating minimum variance. A higher S/N ratio
means better performance for combinatorial parameters. Let ɳij be the S/N ratio for the

response j of scenario i and let Vijk be the simulation result for the response j of
scenario i, in the k th replication; r is the total number of replications. The definition of
the S/N ratio can then be defined as
Equation (1) is used for the larger-the-better responses and Equation (2) is used for the
smaller the- better responses. Besides using the S/N ratio, some authors (Fung 2003,
Lin and Lin 2002) use the mean of the simulation results of all the replications for
optimization. The present research therefore also optimized the mean value for
comparison. After calculating S/N ratios and mean values for each response of all
simulation scenarios, the proposed grey-based Taguchi method then views the multi-
response problem as a MADM problem. Different terminology is commonly used to
describe MADM problems, and in the following description some terms have been
adjusted to conform with usual MADM usage. Thus response was replaced by attribute
and scenario was replaced by alternative in the following.
Step 3: Grey relational generating
When the units in which performance is measured are different for different attributes,
the influence of some attributes may be neglected. This may also happen if some
performance attributes have a very large range. In addition, if the goals and directions
of these attributes are different, this will cause incorrect results in the analysis (Huang
and Liao 2003). It is thus necessary to process all performance values for every
alternative into a comparability sequence, in a process analogous to normalization. This
processing is called grey relational generating in GRA. For a MADM problem, if there
are m alternatives and n attributes, the ith alternative can be expressed as Yi = (yi1, yi2, . .
. , yij , . . . , yin), where yij is the performance value of attribute j of alternative i. The
term yi can be translated into the comparability sequence Xi = (xi1 ,xi2, . . . , xij , . . . , xin)
by the use of one of Equations (3)–(5), where

Equation (3) is used for larger-the-better attributes, Equation (4) is used for smaller-the-
better attributes, and Equation (5) is used for „closer-to-the desired-value-yj -the-better
attributes. Note that the S/N ratio that was calculated in step 2 is a larger-the-better
attribute. Therefore, the proposed grey-based Taguchi method only uses Equation (3)
for grey relational generating.
Step 4: Reference sequence definition
After the grey relational generating procedure, all performance values will be scaled
into [0, 1]. For an attribute j of alternative i, if the value Xij that has been processed by
grey relational generating is equal to 1, or nearer to 1 than the value for any other
alternative, the performance of alternative i is the best one for attribute j . Therefore, an
alternative will be the best choice if all of its performance values are closest to or equal
to 1. However, this kind of alternative does not usually exist. This article defines the
reference sequence X0 as (x01,x02, . . . , x0j , . . . , x0n) = (1, 1, . . . , 1, . . . , 1), and then
aims to find the alternative whose comparability sequence is the closest to the reference
sequence.
Step 5: Grey relational coefficient calculation
The grey relational coefficient is used to determine how close xij is to x0j. The larger the
grey relational coefficient, the closer xij and x0j are. The grey relational coefficient can
be calculated by

Fig 1.5: Relationship between distinguishing coefficient and grey relational
coefficient.
In Equation (6), γ (x0j, xij) is the grey relational coefficient between Xij and X0j and
∆ij = | x0j - xij |
∆min = min {∆ij i=1, 2…. m; j = 1, 2, . . . , n}
∆max = max {∆ij i=1, 2, . . . , m; j = 1, 2, . . . , n}
ζ is the distinguishing coefficient, ζ Є [0, 1]
The purpose of the distinguishing coefficient is to expand or compress the range of the
grey relational coefficient. For example, take the case where there are three
alternatives, a, b and c. If ∆aj = 0.1, ∆bj = 0.4, and ∆cj = 0.9, for attribute j , alternative
a is the closest to the reference sequence After grey relational generating using
Equations (3)–(5), ∆max will be equal to 1 and ∆min will be equal to 0. Figure 2 shows
the grey relational coefficient results when different distinguishing coefficients are
adopted. In Figure 2, the differences between γ (x0j, xaj), γ (x0j, xbj), and γ (x0j, xcj)
always change when different distinguishing coefficients are adopted. But no matter
what the distinguishing coefficient is, the rank order of γ (x0j, xaj), γ (x0j, xbj), and γ (x0j,
xcj) is always the same. The distinguishing coefficient can be adjusted by the decision
maker exercising judgment and different distinguishing coefficients usually produce
different results in GRA.
Step 6: Grey relation grade calculation
After calculating the entire grey relational coefficient γ (x0j, xij) the grey relational
grade can be calculated using

In Equation (7), ┌ (X0,Xi ) is the grey relational grade between Xi and X0.It represents
the level of correlation between the reference sequence and the comparability sequence.
Wj the weight of attribute j and usually depends on decision makers judgments or the
structure of the proposed problem in ∑ Wj𝑛
𝑗=1 = 1. The grey relational grade indicates
the degree of similarity between the comparability sequence& reference sequence. As
mentioned above, on each attribute, the reference sequence represents the best
performance that could be achieved by any among the comparability sequences
therefore if a comparability sequence for an alternative gets the highest grey relational
grade with the reference sequence, the comparability sequence is most similar to the
reference sequence and that alternative would be best choice .
Step 7: Determination of optimal factor levels
According to the principles of the Taguchi method, if the effects of the control factors
on performance are additive, it is possible to predict the performance for a combination
of levels of the control factors by knowing only the main effects of the control factor.
For a factor A that has two levels, 1 and 2, for example, the main effect of factor A at
level 1 (mA1) is equal to the average grey relational grade whose factor A in
experimental scenarios is at level 1, and the main effect of factor A at level 2 (mA2) is
equal to the average grey relational grade whose factor A in experimental scenarios is at
level 2. The higher the main effect is, the better the factor level is. Therefore, the
optimal levels for factor A will be the one whose main effect is the highest among all
levels
1.12 Advantages of WEDM Process
 As continuously travelling wire is used as the negative electrode, so electrode
fabrication is not required as in EDM.
 There is no direct contact between the work piece and the wire, eliminating the
mechanical stresses during machining.
 WEDM process can be applied to all electrically conducting metals and alloys
irrespective of their melting points, hardness, toughness or brittleness.
 Users can run their work pieces over night or over the weekend unattended.

1.13 Disadvantages of WEDM Process
 High capital cost is required for WEDM process.
 There is a problem regarding the formation of recast layer.
 WEDM process exhibits very slow cutting rate.
 It is not applicable to very large work piece.
1.14 Applications of WEDM Process
The present application of WEDM process includes automotive, aerospace, mould, tool
and die making industries. WEDM applications can also be found in the medical,
optical, dental, jewelers industries, and in the automotive and aerospace R & D areas.
The machines ability to operate unattended for hours or even days further increases the
attractiveness of the process. Machining thick sections of material, as thick as 200 mm,
in addition to using computer to accurately scale the size of the part, make this process
especially valuable for the fabrication of dies of various types. The machining of press
stamping dies is simplified because the punch, die, punch plate and stripper, all can be
machined from a common CNC program. Without WEDM, the fabrication process
requires 7 many hours of electrodes fabrication for the conventional EDM technique, as
well as many hours of manual grinding and polishing. With WEDM the overall
fabrication time is reduced by 37%, however, the processing time is reduced by 66%.
Another popular application for WEDM is the machining of extrusion dies and dies for
powder metal (PM) compaction.
1.15 Motivation
Now a day every manufacturing company wants economic and fast way of machining.
WEDM is widely used in most of the manufacturing industries due to its capability of
producing complex geometric surfaces with reasonable accuracy and surface finish. In
order to build up a bridge between quality and productivity and to achieve the same in
an economic way, the present study highlights optimization of WEDM process
parameters to provide high material removal rate (MRR), low electrode consumption
and low surface roughness. So it is required to find the optimum value of machining
parameters on WEDM so that machining can be performed in very economic and fast
way.

CHAPTER 2
LITERATURE SURVEY
2.1 Literature Survey
A literature survey was made on the various optimization techniques that have been
used in the optimization of EDM process parameters. Some of the surveys have been
listed below.
Bhattacharyya B., Gangopadhyay S. and Sarkar B.R has developed mathematical
models for surface roughness, white layer thickness and surface crack density based on
response surface methodology (RSM) approach utilizing experimental data. It
emphasizes the features of the development of comprehensive models for correlating
the interactive and higher-order influences of major machining parameters i.e. peak
current and pulse-on duration on different aspects of surface integrity of M2 Die Steel
machined through EDM. From the obtained test results it is evident that peak current
and pulse-on duration significantly influence various criteria of surface integrity such
as surface roughness, white layer thickness and surface crack density. The optimal
parametric combinations based on the developed models under present set of
experimentations for achieving minimum surface roughness, white layer thickness and
surface crack density are 2A/20μs, 2 A/20μs and 9 A/20μs, respectively. For achieving
desired level of quality of the EDMED surface integrity utilizing present research
findings lead to a significant step towards the goal of accomplishing high precision
machining by EDM. [1]
Tzeng C.J. and Chen R.Y. had proposed an effective process parameter optimization
approach that integrates Taguchi’s parameter design method, response surface
methodology (RSM), a back-propagation neural network (BPNN), and a genetic
algorithm (GA) on engineering optimization concepts to determine optimal parameter
settings of the WEDM process under consideration of multiple responses. Material
removal rate and work-piece surface finish on process parameters during the
manufacture of pure tungsten profiles by wire electrical discharge machining
(WEDM).Specimens were prepared under different WEDM processing conditions

based on a Taguchi orthogonal array of 18 experimental runs. The results were utilized
to train the BPNN to predict the material removal rate and roughness average
properties. Similarly, the RSM and GA approaches were individually applied to search
for an optimal setting. In addition, analysis of variance (ANOVA) was implemented to
identify significant factors for the process parameters, and results from the BPNN with
integrated GA were compared with those from the RSM approach. The results show
that the RSM and BPNN/GA methods are both effective tools for the optimization of
WEDM process parameters. [2]
Tzeng C.J., Yang Y.K., Hsieh M.H. and Jeng M.C. analysed a hybrid method
including a back-propagation neural network (BPNN), a genetic algorithm (GA) and
response surface methodology (RSM) to determine optimal parameter settings of the
EDM process. Material removal rate, electrode wear ratio and work-piece surface finish
on process parameters during the manufacture of SKD61 by electrical discharge
machining (EDM). Specimens were prepared under different EDM processing
conditions according to a Taguchi’s L18 orthogonal array. These experimental runs
were utilized to train the BPNN to predict the material removal rate (MRR), relative
electrode wear ratio (REWR) and roughness average (Ra) properties. Simultaneously,
the RSM and GA approaches were individually applied to search for an optimal setting.
Then, ANOVA was implemented to identify significant factors for the EDM process
parameters. ANOVA indicated that the cutting parameter of discharge current and
pulse-on time is the most significant factors for Ra. the higher discharge energy with
the increase of discharge current and pulse on time leads to a more powerful spark
energy, and thus increased MRR. REWR decreases with increase of pulse on-time
under the same discharge current. The BPNN/GA could be utilized successfully to
predict MRR, REWR and Ra resulting from the EDM process during the manufacture
of SKD61, after being properly trained. Results from the BPNN with integrated GA
were compared with those from the RSM approach. The results show that the proposed
algorithm of GA approach has better prediction and gives confirmation results than the
RSM method. [3]
Lin C.L., Lin J. L. and Ko T.C. has presented the use of grey relational analysis based
on an orthogonal array and the fuzzy-based Taguchi method for the optimisation of the
electrical discharge machining process with multiple process responses. Both the grey

relational analysis method without using the S/N ratio and fuzzy logic analysis are used
in an orthogonal array table in carrying out experiments. Experimental results have
shown that both approaches can optimise the machining parameters (pulse on time,
duty factor, and discharge current) with considerations of the multiple responses
(electrode wear ratio, material removal rate, and surface roughness) effectively and can
greatly improve process responses. It seems that the grey relational analysis is more
straightforward than the fuzzy-based Taguchi method for optimising the EDM process
with multiple process responses. [4]
Panda D.K. and Bhoi R.K have applied ANN to model is checked with the
experimental data. Selection of process parameters as the inputs of the neural network
is based on factorial design of experiment, which enhances the capability of the neural
network because only significant process parameters are considered as the input to the
neural network model. The mathematical consideration of all these complex
phenomena like growth of the plasma channel, energy sharing between electrodes,
process of vaporization, and formation of recast layer, plasma-flushing efficiency and
temperature sensitivity of thermal properties of the work material are a few physical
phenomena that render the machining process highly difficult and stochastic. Therefore,
mathematical prediction of material removal rate when compared with the experimental
results shows wide variation. In such circumstances, the Levenberg-Marquardt back-
propagation algorithm used in this paper, being a second-order error minimization
algorithm, marginalizes the drawback of other back-propagation variants and to predict
the material removal rate. Conclude that the artificial neural network model for EDM
provides faster and more accurate results and the neural network model is less sensitive
to noise. [5]
Mandal D., Pal S.K. and Saha P. made an attempt to model and optimize the
complex electrical discharge machining (EDM) process using soft computing
techniques. Artificial neural network (ANN) with back propagation algorithm is used to
model the process. A large number of experiments have been conducted with a wide
range of current, pulse on time and pulse off time. The MRR and tool wear have been
measured for each setting of current, pulse on time and pulse off time. As the output
parameters are conflicting in nature so there is no single combination of cutting
parameters, which provides the best machining performance. An ANN model has been

trained within the experimental data and various ANN architecture have been studied,
and 3-10-10-2 is found to be the best architecture, with learning rate and momentum
coefficient as 0.6, having mean prediction error is as low as 3.06%.A multi-objective
optimization method, non-dominating sorting genetic algorithm-II is used to optimize
the process. Testing results demonstrate that the model is suitable for predicting the
response parameters. A pareto-optimal set of 100 solutions has been predicted in this
work. [6]
Rao G.K.M., Janardhana G.R., Rao G.H. and Rao M.S. conducted the experiments
by considering the simultaneous effect of various input parameters varying the peak
current and voltage to optimizing the metal removal rate on the Die sinking electrical
discharge machining (EDM). The experiments are carried out on Ti6Al4V, HE15,
15CDV6 and M-250. Multi-perception neural network models were developed using
Neuro solutions approach. Genetic algorithm concept is used to optimize the weighting
factors of the network. It is observed that the developed model is within the limits of
the agreeable error when experimental and network model results are compared for all
performance measures considered. There is considerable reduction in mean square error
when the network is optimized with GA. Sensitivity analysis is also done to find the
relative influence of factors on the performance measures. From the sensitivity analysis
it is concluded that type of material is having highest influence on all performance
measures. It is observed that type of material is having more influence on the
performance measures. Hybrid models are developed for MRR considering all the four
material together which can predict the behavior of these materials when machined on
EDM. [7]
Kansal H.K., Singh S. and Kumara P.aimed to optimize the process parameters
using Response surface methodology to plan and analyze the experiments of powder
mixed electrical discharge machining (PMEDM). Pulse on time, duty cycle, peak
current and concentration of the silicon powder added into the dielectric fluid of EDM
were chosen as variables to study the process performance in terms of material removal
rate and surface roughness. The results identify the most important parameters to
maximize material removal rate and minimize surface roughness. The silicon powder
suspended in the dielectric fluid of EDM affects both MRR and SR. The MRR
increases with the increase in the concentration of the silicon powder. There is

discernible improvement in surface roughness of the work surfaces after suspending the
silicon powder into the dielectric fluid of EDM. The analysis of variance revealed that
the factor peak current and concentration are the most influential parameters on MRR
and SR. The combination of high peak current and high concentration yields more
MRR and smaller SR. The confirmation tests showed that the error between
experimental and predicted values of MRR and SR are within±8% and −7.85% to
3.15%, respectively. [8]
Sanchez H.K., Estrems M. and Faura F. have presented a study attempts to model
based on the least squares theory, which involves establishing the values of the EDM
input parameters namely peak current level, pulse-on time and pulse-off time to ensure
the simultaneous fulfillment of material removal rate (MRR), electrode wear ratio
(EWR) and surface roughness (SR). The inversion model was constructed from a set of
experiments and the equations formulated in the forward model and In this forward
model, the well-known ANOVA and regression models were used to predict the EDM
output performance characteristics, such as MRR, EWR and SR in the EDM process
for AISI 1045 steel with respect to a set of EDM input parameters. As a result, the
predicted values of the parameters showed a good degree of agreement with those
introduced experimentally. For instance, the response surface values of SR = 7.14 μm,
EWR = 6.66% and MRR = 43.1 mm3/min gave the predicted input parameters of I =
9.58 A, ton = 49.53 μs and toff = 17.58 μs, which are close to those implemented in the
experiments as input parameters (I = 9 A, ton = 50 μs and toff = 15 μs). Furthermore,
since the differences between the predicted and experimental values of ton and toff are
expressed in terms of microsecond, the results obtained by the inversion method show a
good agreement to the input parameters introduced into the EDM machine during the
experiments. [9]
Kao J.Y., Tsao C. C., Wang S.S. and Hsu C.Y have proposed an application of the
Taguchi method and grey relational analysis to improve the multiple performance
characteristics of the electrode wear ratio, material removal rate and surface roughness
in the electrical discharge machining of Ti–6Al–4V alloy. The process parameters
selected in this study are discharge current, open voltage, pulse duration and duty
factor. Orthogonal array were used for conducting experiments. The normalized results
of the performance characteristics are then introduced to calculate the coefficient and

grades according to grey relational analysis. As a result, this method greatly simplifies
the optimization of complicated multiple performance characteristics. The optimal
process parameters based on grey relational analysis for the EDM of Ti–6Al–4V alloy
include 5 amp discharge current, 200 V open voltage, 200 μs pulse duration and 70%
duty factor. The optimized process parameters simultaneously leading to a lower
electrode wear ratio, higher material removal rate and better surface roughness are then
verified through a confirmation experiment. The validation experiments show an
improved electrode wear ratio of 15%, material removal rate of 12% and surface
roughness of 19% when the Taguchi method and grey relational analysis are used. [10]
Rao G.K.M. and Rangajanardhaa G. has demonstrated to optimizing the surface
roughness of EDM by considering the simultaneous effect of various input parameters
namely peak current and voltage. The experiments are carried out on Ti6Al4V, HE15,
15CDV6 and M-250. Multi-perception neural network models were developed using
Neuro Solutions package and also genetic algorithm concept is used to optimize the
weighting factors of the network. From the experiments it concluded that at 50 V and
12 A good surface finish is obtained for 15CDV6 and M250.When current increases at
constant voltage surface finish reduces tremendously and for titanium alloy is that it has
good surface finish at voltage 40V and at constant current of 16 A. It is observed that
the developed model is within the limits of the agreeable error when experimental and
network model results are compared. It is further observed that the error when the
network is optimized by genetic algorithm has come down to less than 2% from more
than 5%. Sensitivity analysis is also done to find the relative influence of factors on the
performance measures. It is observed that type of material effectively influences the
performance measures. [11]
George P.M., Raghunath B.K., Manocha L.M. and Warrier A.M. have established
an empirical models correlating process variables that are pulse current, pulse on time
and gap voltage and their interactions with the said response functions named relative
circularity of hole represented by the ratio of standard deviations, overcut, electrode
wear rate (EWR) and material removal rate (MRR) while machining variables. The
experimental investigations on the electrical discharge machining of carbon–carbon
composite plate using copper electrodes of negative polarity. The Experiments are
conducted on the basis of Response surface methodology (RSM) technique. The

models developed reveal that pulse current is the most significant machining parameter
on the response functions followed by gap voltage and pulse on time. These models can
be used for selecting the values of process variables to get the desired values of the
response parameters. These models can be effectively utilized by the process planners
to select the level of parameters to meet any specific EDM machining requirement of
carbon–carbon composite within the range of experimentation. The phenomenon of de-
lamination of carbon–carbon composite, machined using electrical discharge
machining, highly influences estimation of overcut and loss of circularity. [12]
Çaydaş U. and Hasçalik A. studied the case of die sinking EDM process in which he
has taken pulse on-time, pulse off-time and pulse current as input parameters with five
levels. Central composite design (CCD) was used to design the experiments. Here,
modelling of electrode wear (EW) and recast layer thickness (WLT) using response
surface methodology (RSM). ANOVA have been used in study the adequacy of the
modelled equation for the electrode wear and recast layer thickness. They concluded
that the predicted value for EW and WLT are 0.99 and 0.97 respectively. For both EW
and WLT pulse current as found to be most significant factor rather than pulse off-time.
[13]
Habib presented an investigation on EDM process to form a mathematical modelled
equation for material removal rate (MRR), electrode wear ratio (EWR), gap size (GS)
and surface roughness (Ra). The adequacy of the modelled equation has been checked
by using ANOVA (Analysis of variance). The input parameters were taken as pulse on-
time, peak current, gap voltage and SiC particles percentage. He concluded that MRR
increases with the increase of pulse on-time, peak current and with gap voltage and it
decreases with the decrease of SiC percentage. EWR increases with the increase of both
pulse on-time and peak current and decreases with increase of both SiC percentage and
gap voltage. Gap size (GS) reduces by increase of SiC percentage, pulse on-time, peak
current and gap voltage. He modelled equations for the four responses by using RSM
methodology. The modelled equations involves all the significant terms for the
responses. Justification has been done through various experimental analysis and test
results. [14]

Assarzadeh S. and Ghoreishi M. presented an approach on neural network for the
prediction and optimal selection of process parameters in die sinking electrical
discharge machining (EDM) with a flat electrode. For establishment of the process
model a 3-6-4-2 size back propagation neural network was developed. The network
input was taken as current (I), period of pulses (T) and source voltage (V) and material
removal rate (MRR) and surface roughness (Ra) as output parameters. For training and
testing the experimental data was used. Neural model declares the reasonable accuracy
of the process performances under varying machining conditions. The variation sin the
effects was analysed by the neural model. Augmented Lagrange Multiplier (ALM)
algorithm evaluate the corresponding optimum machining conditions through
maximizing MRR which subjects to appropriate operating and prescribed Ra
constraints. Optimization has been done at each level of machining regimes. Machining
regimes such as finishing, semi finishing and roughing from which optimal settings of
machining parameters were obtained. There was no single combination of input
parameters which were optimal for both MRR and Ra. This approach noticed to be
superior because of only experimental data without any mathematical model it is giving
relation input and output variables. They concluded that BP neural network model was
effective for the prediction of MRR and Ra in EDM process. Appropriate trained neural
network model with the ALM neural network positively synthesize the optimal input
conditions for the EDM process. And the optimal setting of input maximizes the MRR.
At the absence of the analytical model process optimization can be done by observing
the experimental data. [15]
Sohani M.S., Gaitonde V.N., Siddeswarappa B. and Deshpande A.S. investigated
the effect of process parameters like pulse on time, discharge current, pulse off time
and tool area through the RSM methodology for effect of tool shape such as triangle,
square, rectangle and circular. The mathematical model was developed for MRR
(material removal rate) and TWR (tool wear rate) using CCD in RSM. The ANOVA
has been used for testing the adequacy of model for the responses. It also resulted that
circular tool shape was best followed by triangular, rectangular and square cross
sections. Interaction between discharge current and pulse on time was highly effective
term for both TWR and MRR. Pulse off time and tool area was individually significant
for both MRR and TWR. MRR increases directly proportional whereas TWR in a non
linear manner. [16]

Chiang K.T. proposed the mathematical modelling and analysis of machining
parameters on the performance in EDM process of Al2O3+ TiC mixed ceramic through
RSM to explore the influence of four input parameters. The input parameters were
taken as discharge current, pulse on time, open discharge voltage and duty factor and
the output parameters as MRR (material removal rate), EWR (electrode wear ratio), and
SR (surface roughness). ANOVA has been used for investigating the influence of
interaction between the factors. Resulted as discharge current and duty factor were the
most statistical significant factors. [17]
Ponappa K., Aravindan S., Rao P.V., Ramkumar J. and Gupta M. studied effects
of EDM on drilled-hole quality as taper and surface finish. The input parameters were
taken as pulse-on time, pulse-off time, voltage gap, and servo speed. ANOVA was used
to identify the significant factors and accuracy for hole. Surface roughness and taper
both depends on the speed and pulse on time. After optimization damaged to the
surface roughness was minimized. [18]
Joshi and Pande reported an intelligent approach for modelling and optimization of
electrical discharge machining (EDM) using finite element method (FEM) has been
integrated with the soft computing techniques like artificial neural networks (ANN) and
genetic algorithm (GA) to improve prediction accuracy of the model with less
dependency on the experimental data. Comprehensive thermo-physical analysis of
EDM process was carried out using two-dimensional axi-symmetric non-linear
transient FEM model etc. to predict the shape of crater, material removal rate (MRR)
and tool wear rate (TWR). A comprehensive ANN based process model is proposed to
establish relation between input process conditions (current, discharge voltage, duty
cycle and discharge duration) and the process responses (crater size, MRR and TWR)
and it was trained, tested and tuned by using the data generated from the numerical
(FEM) model. The developed ANN process model was used in conjunction with the
evolutionary non-dominated sorting genetic algorithm II (NSGA-II) to select optimal
process parameters for roughing and finishing operations of EDM. Two basic ANN
configurations viz. RBFN and BPNN were developed and extensively tested for their
prediction performance and generalization capability. Optimal BPNN based network
architecture 4-5-28-4 was found to give good prediction accuracy (with mean

prediction error of about 7%). The proposed integrated (FEM–ANN–GA) approach
was found efficient and robust as the suggested optimum process parameters were
found to give the expected optimum performance of the EDM process. [19]
2.2 Formulation of Problem
After a comprehensive study of the existing literature, a number of gaps have been
observed in machining of WEDM.
 Literature review depicts that a considerable amount of work has been carried
out by previous investigators to study the machining properties of various
materials. It is also predicted that Taguchi method is a good method for
optimization of various machining parameters as it reduces the number of
experiments.
 Literature review reveals that the researchers have carried out most of the work
on WEDM developments, monitoring and control but very limited work has
been reported on optimization of process variables.
 Most of the researchers have investigated influence of a limited number of
process parameters on the performance measures of WEDM parts.
 The effect of machining parameters on hot working tool steel (D-2) has not
been fully explored using WEDM with brass wire as electrode. It is used for
producing items because of its durability and strength. Hence due to its
applications in various fields, we have to optimize its machining parameters.
The optimization is done by taguchi method in order to get result with good
accuracy.
 Multi-response optimization of WEDM process is another thrust area which has
been given less attention in past studies.

CHAPTER 3
WORK UNDERTAKEN
3.1 Machine Tool
The experiments were carried out on a wire-cut EDM machine (ELEKTRA
SPRINTCUT 734) of Electronica Machine Tools Ltd. installed at Advanced
Manufacturing Laboratory of Mechanical Engineering Department, N.I.T, Kurukshetra,
Haryana, India. The WEDM machine tool (Figure 3.1) has the following specifications:
Design : Fixed column, moving table
Table size : 440 x 650 mm
Max. work piece height : 200 mm
Max. work piece weight : 500 kg
Main table traverse (X, Y) : 300, 400 mm
Auxiliary table traverse (u, v) : 80, 80 mm
Wire electrode diameter : 0.25 mm (Standard)
0.15, 0.20 mm (Optional)
Generator : ELPULS-40 A DLX
Controlled axes : X, Y, U, V simultaneous/
Independent
Interpolation : Linear & Circular
Least input increment : 0.0001mm
Least command input (X, Y, u, v) : 0.0005mm
Input Power supply : 3 phase, AC 415 V, 50 Hz
Connected load : 10 KVA
Average power consumption : 6 to 7 KVA

Fig 3.1: Pictorial View of WEDM Machine Tool
3.2 Preparation of Specimens
The D-2 steel plate of is mounted on the ELECTRONICA SPRINTCUT WEDM machine
tool (Figure 3.1) and specimens of 5mmx5mmx15mm size are cut. The close up view of
plate blank used for cutting the specimens is shown mounted on the WEDM machine
(Figure 3.2).
Fig 3.2: Plate Material Blank Mounted on WEDM Machine

3.3 Experimentation
The experiments were accomplished on an Electronica Sprint cut WEDM machine.
Following steps were followed in the cutting operation:
 The wire was made vertical with the help of verticality block.
 The work piece was mounted and clamped on the work table.
 A reference point on the work piece was set for setting work co-ordinate system
(WCS). The programming was done with the reference to the WCS. The
reference point was defined by the ground edges of the work piece.
 The program was made for cutting operation of the work piece and a profile of
5 mm x 5 mm square was cut.
While performing various experiments, the following precautionary measures were
taken:
 To reduce error due to experimental set up, each experiment was repeated three
times in each of the trial conditions.
 The order and replication of experiment was randomized to avoid bias, if any, in
the results.
 Each set of experiments was performed at room temperature in a narrow
temperature range (32±2o C).
 Before taking measurements of surface roughness, the work piece was cleaned with
acetone.
3.4 Working Mechanism
Work piece to be machined is mounted on the table which is operated by the control
unit. A very small hole is predrilled in the work piece, through which a very thin wire
made of brass or molybdenum is passed and this wire is operated by wire feed
mechanism. Dielectric fluid (distilled water) is passed over the work piece and the wire
(tool) by using pump. When the D.C. supply is given to the circuit, spark is produced
across the gap between the wire and the work piece. When the voltage across the gap
becomes sufficiently large, the high power spark is produced. This spark occurs in an
interval of 10 to 30 microseconds. So thousands of spark discharge occur per second

across the very small gap between the wire and the work piece, which the results in
increasing the temperature. At the high temperature and pressure, work piece metal is
melted, eroded and some of it is vaporized. The metal is thus removed in this way from
the work piece. The removed fine material particles are carried away by dielectric fluid
circulated around it.
3.5 Fixed Parameters
The parameters which are fixed during the process are given in table 3.1 below.
Table 3.1: Fixed Parameters
Sr. No. Parameter Value
1 Work material D2 steel
2 Tool material ( wire ) Brass wire
3 Wire diameter 0.20 mm
4 Height of work material 15.00 mm
5 Servo feed 2050units
6 Wire tension 7grams
7 Wire feed rate 6 mm/min
8 Flushing pressure 1 (15 kg/cm2)
3.6 Parameters and Taguchi Level
The control parameters at three different levels and three different response parameters
considered for multiple performance characteristics in this work are shown in Table 3.2

Table 3.2: Response parameters and control parameters with their levels
Response
Parameters
Material Removal Rate (mm3 /min.)
Electrode Consume (Kg.)
Surface Roughness (μm)
Factor Parameter Levels
L1 L2 L3
A Pulse ON Time 107 114 121
B Pulse OFF
Time
30 35 40
C Peak current 90 150 210
3.7 Experimental details
Design of experiment is an effective tool to design and conduct the experiments with
minimum resources. Orthogonal Array is a statistical method of defining parameters
that converts test areas into factors and levels. Test design using orthogonal array
creates an efficient and concise test suite with fewer test cases without compromising
test coverage. In this work, L9 Orthogonal Array design matrix is used to set the
control parameters to evaluate the process performance. The Table 3.3 shows the
design matrix used in this work.
Table 3.3: Experimental Design Using L9 orthogonal array
Experiment No. A B C
1 1 1 1
2 1 2 2
3 1 3 3

Experiment No. A B C
4 2 1 2
5 2 2 3
6 2 3 1
7 3 1 3
8 3 2 1
9 3 3 2
Specimens of 5mmx5mmx15mm size are cut by WEDM process with the help of
brass wire on D-2 steel as work piece material for each combination of parameters
considered according to the Orthogonal Array. To calculate the material removal rate
and the electrode consumption standard formulas are used and the surface roughness of
the specimen machined was evaluated using a surface roughness-testing machine.
These response parameters are calculated as:
 Metal Removal Rate(MRR)
The material removal rate of the work piece can be calculated using the following
relation.
MRR=k x t x Vc x D
Here k is the kerf,
t is the thickness of the work piece (15 mm),
Vc is the cutting speed in mm/min
D is diameter of wire (mm)
The kerf is expressed as the sum of the wire diameter and twice the wire workpiece
gap. The kerf (cutting width) used to find the metal removal rate determines the

accuracy of the finishing part. The gap between workpiece usually ranges from 0.25 to
0.75mm and is constantly maintained by the computer controlled positioning system
which is 0.27 for our work.
Using the cutting speed values in the formula the Metal Removal Rate (MRR) was
estimated and the values are given in the table 3.4.
Table 3.4: Metal Removal Rate (MRR)
Experiment
No.
A B C Cutting Speed
( mm/Min)
MRR
(mm3/min)
1 107 30 90 1.26 5.127
2 107 35 150 1.19 4.819
3 107 40 210 1.14 4.617
4 114 30 150 2.45 9.922
5 114 35 210 2.42 9.801
6 114 40 90 1.44 5.832
7 121 30 210 3.94 15.95
8 121 35 90 2.17 8.768
9 121 40 150 2.65 10.732
 Electrode Consume
The electrode consume can be calculated using the following equation as:
Electrode consume =f x T x ρ x 𝜋𝐷2
x g /4

Here f is the feed rate (m/min),
T machining time (min),
ρ is density of the work piece material (8.2169 kg/m3)
g is acceleration gravity
D is diameter of wire
Using the values in the formula the electrode consumption was estimated and the
values are given in the table 3.5
Table 3.5: Electrode Consume
Experiment No. Machining Time
(Hours)
Difference ( Min ) Electrode consume
(in kg)
1 285.55 - 286.05 47 .713
2 286.08 - 286.25 15 .227
3 286.28 - 286.49 21 .319
4 286.52 - 287.00 48 .729
5 287.02 - 287.11 09 .136
6 287.13 - 287.29 16 .243
7 287.31 - 287.37 06 .091
8 287.39 - 287.53 14 .212
9 287.54 - 288.02 48 .729
.
 Surface Roughness
Surface roughness was measured using surface roughness tester. The surface roughness
was measured along the sides of every experiment. Two trials were made for every

experiment and the average value was used for the analysis. The surface roughness
values for all the combinations are given below in the table 3.6.
Table 3.6: Surface Roughness
Experiment no. Trial I- Surface
Roughness
Trial II- Surface
Roughness
Surface Roughness
( µm )
1 1.733 0.964 1.34
2 1.231 1.446 1.33
3 1.361 1.198 1.27
4 1.048 1.733 1.39
5 0.996 0.794 0.89
6 1.589 1.420 1.115
7 2.673 2.879 2.74
8 1.550 1.811 1.68
9 0.469 0.592 0.53
3.8 Cost Estimation
Cost estimation of optimization of WEDM process parameters using taguchi method
and grey relational analysisis shown below in Table 3.7.

Table3.7: Cost Detail
Sr. No. Items Cost(Rs)
1 Material (D2 ) cost 1500
2 Distilled water 1100
3 Brass wire roll for WEDM 2000
4 Machining charges 1000
5 Surface roughness testing
charges
800
Total cost 6400
Total cost optimization of WEDM process parameters using taguchi method and grey
relational analysisis = Rs 6400

CHAPTER 4
RESULTS & DISCUSSION
After finding all the observation S/N ratio and Means are calculated and various graph
for analysis is drawn by using Minitab 15 software. The S/N ratio for MRR, surface
roughness and grey grade are calculated on Minitab 15 Software using Taguchi
Method.
4.1 Optimization of MRR
Taguchi method stresses the importance of studying the response variation using the
signal–to–noise (S/N) ratio, resulting in minimization of quality characteristic variation
due to uncontrollable parameter. The metal removal rate was considered as the quality
characteristic with the concept of "the larger-the-better".
The S/N ratio for the larger-the-better is:
S/N = 10 ∑ (4.1)
Where n is the number of measurements in a trial/row, in this case, n=1 and y is the
measured value in a run/row. The S/N ratio values are calculated by taking into
consideration equation 4.1 with the help of software Minitab 15. The MRR values
measured from the experiments and their corresponding S/N ratio values are listed in
Table-4.1.
Table- 4.1: Experimental Result and Corresponding S/N Ratio for MRR
Sr.
No.
Pulse
ON
(µs)
Pulse
OFF
(µs)
Peak
current(A)
MRR
(mm3/s)
S/N Mean
1 107 30 90 5.127 14.1973 5.127
2 107 35 150 4.819 13.6591 4.819
3 107 40 210 4.617 13.2872 4.617
4 114 30 150 9.922 19.9320 9.922

Sr.
No.
Pulse
ON
(µs)
Pulse
OFF
(µs)
Peak
current(A)
MRR
(mm3/s)
S/N Mean
5 114 35 210 9.801 19.8254 9.801
6 114 40 90 5.832 15.3164 5.832
7 121 30 210 15.95 24.0552 15.950
8 121 35 90 8.768 18.8580 8.768
9 121 40 150 10.732 20.6136 10.732
4.1.1 Result and discussion
Regardless of the category of the performance characteristics, a greater S/N value
corresponds to a better performance. Therefore, the optimal level of the machining
parameters is the level with the greatest value.
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22
20
18
16
14
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22
20
18
16
14
pulse ON
MeanofSNratios
pulse off
peak current
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better
Fig. 4.1: Effect of WEDM Parameters on M.R.R. for S/N Ratio

 Pulse ON
The effect of parameter Pulse ON on the metal removal rate values is shown in Fig. 4.1
for S/N ratio and its optimum value is 121µs.
 Pulse OFF
The effect of parameters Pulse OFF on the metal removal rate values is shown in Fig.
4.1 for S/N ratio and its optimum value is 30µs.
 Peak current
The effect of parameters Peak current on the metal removal rate values is shown in Fig.
4.2 for S/N ratio. So the optimum of Peak current is 210 A.
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12
10
8
6
4
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21015090
12
10
8
6
4
pulse ON
MeanofMeans
pulse OFF
peak current
Main Effects Plot for Means
Data Means
Fig. 4.2: Effect of WEDM Parameters on M.R.R. for Means
4.2 Optimization of Surface Roughness
Surface roughness is also a quality parameter; it should be as low as possible. So we
have chosen the smaller the better signal to noise ratio.
The S/N ratio for the smaller-the-better is:

S/N = -10 ∑y2} (4.2)
Where n is the number of measurements in a trial/row, in this case, n=1 and y is the
measured value in a run/row. The S/N ratio values are calculated by taking into
consideration equation 4.2 with the help of software Minitab 15. The surface roughness
values measured from the experiments and their corresponding S/N ratio values are
listed in Table-4.2. We have completed our surface testing from ACE COLLEGE
MEETHAPUR.
Table- 4.2: Experimental Result and Corresponding S/N Ratio for Surface Roughness
Sr.
No.
Pulse
ON
(µs)
Pulse
OFF
(µs)
Peak
current
(A)
Surface
Roughness
(µm)
S/N Mean
1 107 30 90 1.34 -2.54210 1.34
2 107 35 150 1.33 -2.47703 1.33
3 107 40 210 1.27 -2.07607 1.27
4 114 30 150 1.39 -2.86030 1.39
5 114 35 210 0.89 1.01220 0.80
6 114 40 90 1.115 -0.94550 1.15
7 121 30 210 2.74 -8.75501 2.74
8 121 35 90 1.68 -4.50619 1.68
9 121 40 150 0.53 5.51448 0.53

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-1.2
-2.4
-3.6
-4.8
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0.0
-1.2
-2.4
-3.6
-4.8
puls onMeanofSNratios off
pc
Data Means
Signal-to-noise: Smaller is better
Fig. 4.3 Effect of WEDM Parameters on Surface roughness for S/N Ratio
Regardless of the category of the performance characteristics, a smaller S/N value
parameters is the level with the smallest value.
 Pulse ON
The effect of parameters Pulse ON on the Surface roughness values is shown in Fig. 4.3
for S/N ratio and its optimum value is 114µs.
 Pulse OFF
The effect of parameters Pulse OFF on the Surface roughness values is shown in Fig.
4.3 for S/N ratio. So the optimum Pulse OFF is 40µs.
 Peak current
The effect of parameters peak current on the Surface roughness values is shown in Fig.
4.3 for S/N ratio. So the optimum peak current is 150 A.

121114107
1.8
1.6
1.4
1.2
1.0
403530
21015090
1.8
1.6
1.4
1.2
1.0
puls onMeanofMeans off
pc
Data Means
Fig. 4.4 Effect of WEDM Parameters on Surface roughness for mean Ratio
4.3 Optimization of Electrode Consume
Electrode consume is also a quality parameter, it should be as low as possible. So we
have chosen the smaller the better signal to noise ratio. The electrode consumed values
measured from the experiments and their corresponding S/N ratio values are listed in
Table-4.3.
Table- 4.3: Experimental Result and Corresponding S/N Ratio for Electrode Consume
Sr.
No.
Pulse
ON
(µs)
Pulse
OFF
(µs)
Peak
current
(A)
Electrode
consume
(Kg)
S/N Mean
1 107 30 90 0.713 2.9382 0.713
2 107 35 150 0.227 12.8795 0.227
3 107 40 210 0.319 9.9242 0.319

Sr.
No.
Pulse
ON
(µs)
Pulse
OFF
(µs)
Peak
current
(A)
Electrode
consume
(Kg)
S/N Mean
4 114 30 150 0.729 2.7454 0.729
5 114 35 210 0.136 17.3292 0.136
6 114 40 90 0.243 12.2879 0.243
7 121 30 210 0.091 20.8192 0.091
8 121 35 90 0.212 13.4733 0.212
9 121 40 150 0.729 2.7454 0.729
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12.5
10.0
7.5
5.0
403530
21015090
15.0
12.5
10.0
7.5
5.0
pulse ON
MeanofSNratios
pulse off
peak current
Data Means
Signal-to-noise: Smaller is better
Fig. 4.5 Effect of WEDM Parameters on Electrode Consumed for S/N Ratio

Regardless of the category of the performance characteristics, a smaller S/N value
 Pulse ON
The effect of parameters Pulse ON on the Electrode Consumed values is shown in Fig.
4.5 for S/N ratio. So the optimum Pulse ON is 121µs.
 Pulse OFF
The effect of parameters Pulse OFF on the Electrode Consumed values is shown in Fig.
4.5 for S/N ratio. So the optimum Pulse OFF 35µs.
 Peak current
The effect of parameters peak current on the Electrode Consumed values is shown in
Fig. 4.5 for S/N ratio. So the optimum peak current is 150 A.
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0.5
0.4
0.3
0.2
403530
21015090
0.6
0.5
0.4
0.3
0.2
pulse on
MeanofMeans
pulse off
peak current
Data Means
Fig. 4.6 Effect of WEDM Parameters on electrode consumed for mean Ratio

4.4 Optimization of MRR, Electrode consume and Surface Roughness(Grey
Analysis)
For grey analysis of surface roughness, electrode consume and material removal rate
following procedure is followed:
4.4.1 Experimental design and signal to noise calculations
As we have already done this step .Design of experiment is done in chapter-3 where
orthogonal array L9 is find out from degree of freedom and signal to noise for material
removal rate, electrode consume and surface roughness is find out in the 4.1, 4.2 and
4.3 articles of this chapter. Hence the combined result of material removal rate,
electrode consume and surface roughness is shown in table 4.5.
Table 4.4 Experimental results
Sr.
No.
Pulse
ON
(µs)
Pulse
OFF
(µs)
Peak
current(A)
S/N(MRR)
(mm3/s)
S/N
(S.R)
S/N
(E.C)
1 140 18 1 14.1973 -2.54210 2.9382
2 140 18 1.5 13.6591 -2.47703 12.8795
3 140 18 2 13.2872 -2.07607 9.9242
4 140 29 1 19.9320 -2.86030 2.7454
5 140 29 1.5 19.8254 1.01220 17.3292
6 140 29 2 15.3164 -0.94550 12.2879
7 140 45 1 24.0552 -8.75501 20.8192
8 140 45 1.5 18.8580 -4.50619 13.4733
9 140 45 2 20.6136 5.51448 2.7454

4.4.2 Grey relational generation
When the units in which performance is measured are different for different attributes,
the influence of some attributes may be neglected. This may also happen if some
performance attributes have a very large range. In addition, if the goals and directions
of these attributes are different, this will cause incorrect results in the analysis. It is thus
necessary to process all performance values for every alternative into a comparability
sequence, in a process analogous to normalization. This processing is called grey
relational generating in GRA.
The main purpose of grey relational generating is transferring the original data into
comparability sequences.
4.3
4.4
4.3 equation is used for larger the better attribute (MRR) where as 4.4 equation is used
for smaller the better (surface roughness and electrode consume). The results are shown
in Table 4.5.
Table 4.5 Grey Relational Generation
Sr.
No.
S/N
(MRR)
S/N
(S.R)
S/N
(E.C)
G.R.G
(MRR)
G.R.G
(S.R)
G.R.G
(E.C)
1 14.1973 -2.54210 2.9382 0.084 0.564 0.989
2 13.6591 -2.47703 12.8795 0.034 0.560 0.439
3 13.2872 -2.07607 9.9242 0 0.531 0.602
4 19.9320 -2.86030 2.7454 0.617 0.586 1
5 19.8254 1.01220 17.3292 0.607 0.3115 0.193
6 15.3164 -0.94550 12.2879 0.188 0.452 0.472

Sr.
No.
S/N
(MRR)
S/N
(S.R)
S/N
(E.C)
G.R.G
(MRR)
G.R.G
(S.R)
G.R.G
(E.C)
7 24.0552 -8.75501 20.8192 1 1 0
8 18.8580 -4.50619 13.4733 0517 0.702 0.406
9 20.6136 5.51448 2.7454 0.680 0 1
4.4.3 Grey relational deviation sequences
After the grey relational generating procedure, all performance values will be scaled
into [0, 1]. For an attribute j of alternative i, if the value xij that has been processed by
grey relational generating is equal to 1, or nearer to 1 than the value for any other
alternative, the performance of alternative i is the best one for attribute j. Therefore, an
alternative will be the best choice if all of its performance values are closest to or equal
to 1.The deviation sequence is find out by the equation 4.5. For S/N ratios, for
example,∆11 = |1 − 0.084| =.916
4.5
Table 4.6 Grey Relational Deviation Sequence
Sr.
No.
G.R.G
(MRR)
G.R.G
(S.R)
G.R.G
(E.C)
D.S
(MRR)
D.S
(S.R)
D.S
(E.C)
1 0.084 0.564 0.989 0.916 0.436 0.011
2 0.034 0.560 0.439 0.960 0.440 0.561
3 0 0.531 0.602 1 0.469 0.398
4 0.617 0.586 1 0.383 0.414 0
5 0.607 0.3115 0.193 0.393 0.684 0.807

Sr.
No.
G.R.G
(MRR)
G.R.G
(S.R)
G.R.G
(E.C)
D.S
(MRR)
D.S
(S.R)
D.S
(E.C)
6 0.188 0.452 0.472 0.812 0.548 0.528
7 1 1 0 0 0 1
8 0517 0.702 0.406 0.483 0.297 0.594
9 0.680 0 1 0.320 1 0
4.4.4 Grey relational coefficient calculation
In Table 4.7, X0 is the reference sequence/deviation sequence. After calculating ∆ij
,∆max , and ∆min, all grey relational coefficients can be calculated using Equation 4.6.
For S/N ratios, for example,∆11 = |1 − 0.084| =.916, ∆max = 1 and ∆min = 0 and ζ = 0.33,
then γ (x01, x11) = (0 + 0.33 × 1)/(0.916 + 0.33 × 1)= 0.266. The results for the grey
relational coefficient are shown in Table 4.7.
ζ is the distinguishing coefficient, ζ ∈ [0, 1] The purpose of the distinguishing
coefficient is to expand or compress the range of the grey relational coefficient.
4.6
Table 4.7 Grey Relational Coeficients
Sr.
No.
D.S
(MRR)
D.S
(S.R)
D.S
(E.C)
G.R.C
(MRR)
G.R.C
(S.R)
G,R,C
(E.C)
1 0.916 0.436 0.011 0.266 0.433 0.968
2 0.960 0.440 0.561 0.256 0.430 0.372
3 1 0.469 0.398 0.249 0.415 0.455

Sr.
No.
D.S
(MRR)
D.S
(S.R)
D.S
(E.C)
G.R.C
(MRR)
G.R.C
(S.R)
G,R,C
(E.C)
4 0.383 0.414 0 0.465 0.445 1
5 0.393 0.684 0.807 0.458 0.327 0.292
6 0.812 0.548 0.528 0.290 0.377 0.386
7 0 0 1 1 1 0.249
8 0.483 0.297 0.594 0.408 0.527 0.359
9 0.320 1 0 0.509 0.249 1
4.4.5 Grey relational grade calculation
After calculating the entire grey relational coefficient γ (x0j, xij ), the grey relational
grade can be calculated using
4.7
Here wj =0.33
Grey relational grade represents the level of correlation between the reference sequence
and the comparability sequence. Wj the weight of attribute j and usually depends on
decision makers judgments or the structure of the proposed problem in ∑ Wj𝑛
𝑗=1 = 1.
The grey relational grade indicates the degree of similarity between the comparability
sequence& reference sequence
4.4.6 Determination of optimal factor levels
The optimal factor levels are obtained by using Minitab 15software.In this grey
relational grade is used as the output parameter and signal to noise ratio for larger the
better is find out .Then we get the optimal value of output factors can be find out.

Table 4.8 Grey Relational Grade
S.NO. G.R.G S/N(G.R.G) Mean(G.R.G)
1 0.555 -5.1141 0.555
2 0.352 -9.0691 0.352
3 0.345 -9.2436 0.345
4 0.636 -3.9309 0.636
5 0.359 -8.89811 0.359
6 0.351 -9.0939 0.351
7 0.649 -2.5104 0.649
8 0.431 -7.3105 0.431
9 0.586 -4.6420 0.586
4.5 Discussion of Result
Regardless of the category of the performance characteristics, a greater S/N value

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-6
-8
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21015090
-4
-6
-8
pulse on
MeanofSNratios
pulse off
peak current
Data Means
Signal-to-noise: Larger is better
Fig. 4.7 Effect of WEDM Parameters on Surface roughness, Electrode consume and
MRR for S/N Ratio
 Pulse ON
The effect of parameters Pulse ON on the Surface roughness, Electrode Consume and
RR values are shown in Fig. 4.7 for S/N ratio. Its effect is that as far as speed increases
MRR decrease and surface roughness increases. So the optimum Pulse ON is level 3
i.e. 121µs.
 Pulse OFF
The effect of parameters Pulse OFF on the Surface Roughness, Electrode Consume and
MRR values are shown in Fig. 4.7 for S/N ratio. So the optimum Pulse OFF is level1
i.e. 30µs
 Peak Current
The effect of parameters Peak Current on the Surface roughness values is shown in Fig.
4.5 for S/N ratio. So the optimum Peak Current is level 1 i.e. 90 A

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0.5
0.4
403530
21015090
0.6
0.5
0.4
pulse onMeanofMeans pulse off
peak current
Data Means
Fig.4.8: Effect of WEDM Parameters on Surface roughness, Electrode consume and
MRR for Mean
Table 4.9 Optimum Value of Parameter According to S/N Ratio
Pulse ON
(µs)
Pulse OFF
(µs)
Peak Current
(A)
Grade
121 35 90 0.431
Hence the grey grade is 0.431 corresponding to 121 µs, 35 µs and 90 A.
4.6 Confirmation Test
Once the optimal level of the design parameters has been selected the final step is to
predict and verify the improvement of the quality characteristic using the optimal level
of the design parameters. For confirmation a final experiment is performed with
optimum value of WEDM parameters and it is founded that material removal rate is
improved, electrode consume and surface roughness is decreased. Hence the parameters

shown in Table 4.9 are optimum value for maximum material removal rate and low
surface roughness and electrode consume.

CHAPTER 5
CONCLUSIONS AND FUTURE SCOPE
5.1 Conclusions
In the earlier chapters, the effects of process variables on response characteristics
(material removal rate, surface roughness and electrode consumption) of the wire
electric discharge machining (WEDM) process have been discussed. An optimal set of
process variables that yields the optimum quality features to machined parts produced
by WEDM process has also been obtained. The important conclusions from the present
research work are summarized in this chapter.
 In this work, an attempt was made to determine the important machining the
parameters of brass material for the performance measures like MRR, electrode
consume and SR separately in WEDM process.
 Factors like the pulse duration and the feed rate have been found to play as
significant role in rough cutting operations for the maximization of metal
removal rate, minimization of electrode consumption and minimization of
surface roughness Taguchi's experimental design (L9 orthogonal array) is used
to obtain the optimum machining parameters for the maximization of metal
removal rate, minimization of electrode consumption and minimization of
surface roughness.
5.2 FUTURE WORK
Although the WEDM machining has been thoroughly investigated for D-2 Steel work
material, still there is a scope for further investigation. The following suggestions may
prove useful for future work:
 L9 orthogonal array does not provide interaction between different parameters.
Therefore higher order orthogonal array (OA) can be incorporate all possible
interactions of the process parameters.
 The effect of process parameters such as flushing pressure, conductivity of
dielectric, wire diameter, work piece height, electric flow rate etc. may also be
investigated.

 Other performance criteria such as the skewness, waviness and white layer
depth of the wire electro- discharge machined job surface might be investigated
using the same approach presented here.
 Efforts should be made to investigate the effects of WEDM process parameters
on performance measures in a cryogenic cutting environment.

References
[1]Bhattacharyya B., Gangopadhyay S. and Sarkar B.R., Modelling and analysis of
EDM job surface integrity, Journal of Materials Processing Technology, 189 (2007):
pp. 169–177.
[2] Tzeng C.J. and Chen R.Y., Optimization of electric discharge machining process
using the response surface methodology and genetic algorithm approach, International
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ratio and surface roughness , International Journal of Advance Manufacturing
Technology, 57(2011): pp.189–201.
[10] Kao J.Y., Tsao C. C., Wang S.S. and Hsu C.Y., Optimization of the EDM
parameters on machining Ti–6Al–4V with multiple quality characteristics, International
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[11] Rao G.K.M. and Rangajanardhaa G., Development of hybrid model and
optimization of surface roughness in electric discharge machining using artificial neural
networks and genetic algorithm, Journal of Materials Processing Technology,
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[12] George P.M., Raghunath B.K., Manocha L.M. and Warrier A.M., Modelling of
machinability parameters of carbon–carbon composite—a response surface approach,
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[14] Habib S.S., Study of the parameters in electrical discharge machining through
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[15] Assarzadeh S. and Ghoreishi M., Neural-network-based modeling and
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Advanced Manufacturing Technology, 39(2008): pp. 488–500.
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Investigations into the effect of tool shapes with size factor consideration in sink
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2755.

APPENDIX –I
IMPORTANT RESOURCES
1. NIT Kurukshetra for the machining on WEDM.
2. Ambala collage of Engineering, Mithapur for surface testing.

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