A CONCISE REVIEW ON
OPTIMIZATION OF MACHINING
PROCESS VARIABLES IN WEDM
SANTOSH PATRO
Mechanical Engineering Department
Centurion University, Paralakhemundi, Odisha, India – 761211
[email protected]
P SRINIVASA RAO
Mechanical Engineering Department
Centurion University, Paralakhemundi, Odisha, India – 761211
[email protected]
Abstract: To fulfil the industrial requirements of good surface finish and machining of complex shape
geometries of hard-to-machine materials, conventional machining process are now being replaced by
traditional machining processes. Wire Electro Discharge machining (WEDM) is one of the
non-traditional machining processes. Selection of optimal values of affecting parameters can give the desired
results of higher material removal rate, lower surface roughness, lower kerf width etc. In this paper, a
review has been done on the optimization of WEDM machining process on the basis of wire electrode
used, input parameters, output parameters and the optimization techniques used. This study will be
useful for the researchers in selecting the affecting parameters.
Keywords: Taguchi Technique, Grey Relational Analysis (GRA), Response Surface Method (RSM), Artificial
Neural Network (ANN), Genetic Algorithm (GA), Utility Concept.
1.
Introduction
With the advancement in material science, new materials have been developed having good mechanical
properties like toughness, hardness, wear resistance etc. Machining of these materials using conventional
machining has led to poor surface finish and inaccuracy. So, nowadays a number of non-traditional machining
processes are used to get the desired surface roughness and accuracy. WEDM is one of the most widely used
machining process to machine complex intricate shapes irrespective of the hardness of the workpiece material.
This process is extensively used in mould and die making industries, aerospace industries and automobile
industries etc.
WEDM is an electro thermal process in which material is removed by melting and evaporation by a series of
sparks occurring between the workpiece and a wire which act as an electrode. The gap between the workpiece
and the wire is flooded with dielectric fluid which enhances the machining process. Generally, a wire of 0.1 to
0.3 mm diameter is used as electrode and workpiece is mounted on a Computerized Numerical Control (CNC)
worktable. A gap of 0.025 to 0.05 mm is maintained between the workpiece and the wire. The wire is
continuously fed during the machining to get the desired machined surfaces [46].
In order to have good quality product, it is necessary to go for optimization of process parameters. A lot of
work has been done by a number of researchers in this direction. In this paper, a review has been done on the
optimization of WEDM process on the basis of various process parameters, i.e., wire electrode used, input
parameters, output parameters and optimization techniques used.
2.
Literature Review
2.1. On the basis of wire used
Table 1. Different wires used during WEDM machining process.
Sl.
No.
Authors
Wire used
Copper wire
Zinc coated
co
pp
er
wire
Brass wire
Zinc coated
Brass wire
Diff
usion
Anne
aled Br
ass
wire
Mol
ybde
num
wire
1 Durairaj
(2013)
√
2 Shayan
(2013)
√
3 Tilekar
(2014)
√
4 Goswami
(2014)
√
5 Ugrasen
(2014)
√
6 Saedon
(2014)
√
7 Mathew
(2014)
√
8 SelvaKumar
(2014)
√
9 Ugrasen
(2014)
√
10 Manjaiah
(2014)
√
11 Sharma
(2014)
√
12 Bobbili
(2015)
√
13 Dongre
(2015)
√
14 Sharma
(2015)
√
15 Rao
(2015)
√
16 Ugrasen
(2015)
√
17 Reddy
(2015)
√
18 Raj
(2015)
√
19 Sinha
(2015)
√
20 Maher
(2015)
√
21 Patel
(2015)
√
22 Dabade
(2016)
√
23 Patil
(2016)
√
√
24 Saha
(2016)
√
√
25 Devarasiddappa
(2016)
√
26 Pramanick
(2016)
√
27 Goyal
(2017)
√
28 Goswami
(2017)
√
29 Joshi
(2017)
√
30 Majumder
(2017)
√
31 Unune
(2017)
√
32 Gamage
(2017)
√
√
33 Kumar
(2017)
√
34 Arikatla
(2017)
√
35 Ajay
(2017)
√
36 Gurupavan
(2017)
√
37 Mouralova
(2018)
√
38 Sonawane
(2018)
√
39 Ramanan
(2018)
√
2.2. On the basis of Input parameters
Input parameters play a major role in the quality of machining. Therefore, proper selection of input parameters
should be done while machining in WEDM in order to get the desired outcomes. It has been found that an
increase in input current results in increased MRR as well as increased surface roughness. Similar effect is also
observed when spark voltage is increased. However, an increase in spark frequency leads to improved surface
roughness. A decrease in the gap between the electrode and workpiece results in lower MRR, better surface
finish and higher accuracy. Also, an increase in pulse duration decreases MRR and deteriorates surface finish
[15]. Table 2 gives a brief review on different input parameters taken by different researchers during WEDM
machining.
Table 2. Different Input Parameters taken during WEDM machining process.
Sl.
No.
Authors
Input Parameters
Pulse O
N
Time
Pulse OFF
Time
Peak Current
Servo Vol
tage
Wire Feed
Ra
te
Wire Tension
Wire Offset
Dielectric
Pressure
Wire Type
Bed Spee
d
1 Durairaj
(2013)
√
√
√
√
2 Shayan
(2013)
√
√
√
√
√
3 Tilekar
(2014)
√
√
√
√
4 Goswami
(2014)
√
√
√
√
√
√
5 Ugrasen
(2014)
√
√
√
√
6 Saedon
(2014)
√
√
√
√
7 Mathew
(2014)
√
√
√
√
√
√
8 SelvaKumar
(2014)
√
√
√
√
9 Ugrasen
(2014)
√
√
√
√
10 Manjaiah
(2014)
√
√
√
√
√
11 Sharma
(2014)
√
√
√
√
12 Bobbili
(2015)
√
√
√
√
13 Sharma
(2015)
√
√
√
√
√
14 Rao
(2015)
√
√
√
√
15 Ugrasen
(2015)
√
√
√
√
16 Reddy
(2015)
√
√
√
√
17 Raj
(2015)
√
√
√
18 Sinha
(2015)
√
√
√
√
19 Maher
(2015)
√
√
√
20 Dabade
(2016)
√
√
√
√
√
√
21 Patil
(2016)
√
√
√
√
22 Saha
(2016)
√
√
√
√
√
23 Devarasiddappa
(2016)
√
√
√
√
24 Pramanick
(2016)
√
√
√
√
√
25 Goyal
(2017)
√
√
√
√
√
√
26 Goswami
(2017)
√
√
√
√
27 Joshi
(2017)
√
√
√
28 Majumder
(2017)
√
√
√
√
29 Unune
(2017)
√
√
30 Gamage
(2017)
√
√
√
√
31 Kumar
(2017)
√
√
√
√
32 Arikatla
(2017)
√
√
√
√
33 Ajay
(2017)
√
√
√
√
35 Mouralova
(2018)
√
√
√
√
√
36 Sonawane
(2018)
√
√
√
√
√
√
37 Ramanan
(2018)
√
√
√
√
38 Sen
(2018)
√
√
√
√
√
2.3. On the basis of Output parameters
WEDM machining is generally used to machine complex intricate shapes in hard-to-machine materials with
required finish and accuracy in order to meet industrial requirements. It is found that MRR, WWR and
topological parameters are governed by energy content of the pulse and the rate at which they are supplied. In
addition to these, other controlling parameters like servo sensitivity, gap width and dielectric parameters etc.
also contribute to the output performance [23]. The different output parameters taken by different researchers
during WEDM machining is shown in Table 3.
Table 3. Different Output Parameters considered during WEDM machining process.
Sl.
No.
Authors
Output Parameters
Ma
terial
Rem
ov
al R
ate
(M
RR
)
Surface
Roughness
(S
R)
Kerf Width
Dimensional
Devi
ati
on
Wire Wear
Rate
(WW
R
)
Overcut
Cutting Speed
Ma
chining Time
Accur
acy
Leng
th o
f
C
ut
1 Durairaj
(2013)
√
√
2 Shayan
(2013)
√
√
√
3 Tilekar
(2014)
√
√
4 Goswami
(2014)
√
√
5 Ugrasen
(2014)
√
√
√
6 Saedon
(2014)
√
√
√
7 Mathew
(2014)
√
√
√
8 SelvaKumar
(2014)
√
√
9 Ugrasen
(2014)
√
√
√
10 Manjaiah
(2014)
√
√
11 Sharma
(2014)
√
12 Bobbili
(2015)
√
√
13 Dongre
(2015)
√
√
√
14 Sharma
(2015)
√
15 Rao
(2015)
√
√
16 Ugrasen
(2015)
√
√
√
17 Reddy
(2015)
√
√
√
18 Raj
(2015)
√
√
19 Sinha
(2015)
√
√
√
20 Maher
(2015)
√
√
21 Patel
(2015)
√
√
√
22 Dabade
(2016)
√
√
√
√
23 Patil
(2016)
√
√
√
24 Saha
(2016)
√
√
√
25 Devarasiddappa
(2016)
√
26 Pramanick
(2016)
√
√
27 Goyal
(2017)
√
√
28 Goswami
(2017)
√
√
√
29 Joshi
(2017)
√
√
31 Unune
(2017)
√
√
32 Gamage
(2017)
√
33 Kumar
(2017)
√
√
√
34 Arikatla
(2017)
√
√
√
35 Ajay
(2017)
√
√
36 Gurupavan
(2017)
√
√
√
√
37 Mouralova
(2018)
√
38 Sonawane
(2018)
√
√
√
39 Ramanan
(2018)
√
√
40 Sen
(2018)
√
√
√
2.4. On the basis of Optimization techniques used
Different optimization techniques have been used to get the optimized output responses, i.e., higher MRR, lower
WWR, lower surface roughness, lower kerf width and higher accuracy. Initially single response optimization
was done using simple Taguchi technique. But in order to have multi-response optimization simple Taguchi
technique is not suitable. Therefore, a number of multi-response techniques have been developed by different
researchers in order to optimize the output responses. Grey Relational Analysis (GRA), Response Surface
Methodology (RSM), Artificial Neural Network (ANN), Genetic Algorithm (GA) and Utility concept are some
of the most commonly used multi-response optimization techniques. Nowadays, hybrid multi-response
optimization techniques are used where two or more techniques are combined in order to get better results.
Table 4 shows the different optimization techniques used by different researchers during WEDM machining.
Table 4. Different Optimization Techniques used in WEDM machining process.
Sl.
No
.
Authors
Optimization Techniques
Taguchi Technique
Grey Rel
ati
on
al
A
n
al
ysi
s (G
RA
)
R
espo
n
se Surfa
ce
M
ethod (R
SM
)
Utility Co
ncept
Artificial
Neu
ral Ne
tw
or
k (
A
N
N
)
Genetic Alg
or
ithm (G
A)
Particle S
w
ar
m Me
th
od
(P
SM
)
Regre
ssi
on
A
n
al
ysi
s
Non-Dominated Sor
ting Genetic
Al
gori
thm
–
I
I
(NSG
A
-
II
)
Pri
n
ci
pal
Co
mpone
n
t A
n
al
ysi
s (
P
C
A
)
Hy
brid GR
A
– PC
A
M
et
hod
Adapti
ve Ne
uro
– Fuzz
y Inference
sy
ste
m
(
AN
FIS
)
Grey – Fuzz
y Model
ANN –
Fuzzy
–
TLBO Hy
brid
techniq
u
e
1 Durairaj
(2013)
√
√
2 Shayan
(2013)
√
√
3 Tilekar
(2014)
√
4 Goswami
(2014)
√
√
5 Ugrasen
(2014)
√
√
6 Saedon
(2014)
√
√
7 Mathew
(2014)
√
√
8 SelvaKumar
(2014)
√
9 Ugrasen
(2014)
√
√
10 Manjaiah
(2014)
√
√
11 Sharma
(2014)
√
√
√
12 Bobbili
(2015)
√
√
13 Dongre
(2015)
√
√
14 Sharma
(2015)
√
√
√
15 Rao
(2015)
√
√
√
16 Ugrasen
(2015)
√
17 Reddy
(2015)
√
√
19 Sinha
(2015)
√
√
20 Maher
(2015)
√
√
21 Dabade
(2016)
√
22 Patil
(2016)
√
23 Saha
(2016)
√
√
24 Devarasiddappa
(2016)
√
√
25 Pramanick
(2016)
√
√
√
26 Goyal
(2017)
√
27 Goswami
(2017)
√
28 Joshi
(2017)
√
√
29 Majumder
(2017)
√
√
√
30 Unune
(2017)
√
31 Gamage
(2017)
√
32 Kumar
(2017)
√
√
33 Arikatla
(2017)
√
√
34 Ajay
(2017)
√
35 Gurupavan
(2017)
√
√
36 Mouralova
(2018)
√
37 Sonawane
(2018)
√
√
√
38 Ramanan
(2018)
√
√
39 Sen
(2018)
√
√
3.
Conclusion
In the present study it is found that a lot of work has been done by the researchers in order to optimize the
machining parameters in wire EDM. Still researchers are trying for better combination of machining parameters
in order to further improve the output performances. From the review it can be concluded that there is scope for
improvement of output performances in wire EDM machining in future.
4.
Acknowledgements
The authors would thank Centurion University, Paralakhemundi Campus, Odisha, India for their support in
collecting the data.
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