THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM PROCESS

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 5, May (2014), pp. 180-188 © IAEME
180
THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS
WORKPIECE USING WEDM PROCESS
Manpreet Singh1
, Amandeep Singh Bansal2
, Sanjeev Kumar3
1-3
(Dept of Mechanical Engg., CTIEMT, Shahpur, India,)
ABSTRACT
Wire electrical discharge machining (WEDM) is a specialized thermal machining process
which is capable of accurate machining of parts which have varying hardness, complex shapes and
sharp edges that are hard to be machined by the traditional machining processes. . Predictions on the
Material Removal Rate of workpieces in WEDM have been reported in the past. In the present study
an analysis has been done to evaluate the Material Removal Rate of MS workpiece using WEDM
process with different types of wire electrodes. It has been found that the effect of material of wire,
on percentage improvement in Material Removal Rate (MRR) is prominent. Result shows that
improvement in MRR is higher with copper wire as compared to other wire electrodes. Also MRR
reduces gradually as the value of current in the circuit was increased.
Keywords: Wire EDM, Material Removal Rate, Material of Wire Electrode, Brass Wire,
Zinc Coated Brass Wire.
1. INTRODUCTION
Wire electrical discharge machining (WEDM) is a specialized thermal machining process
which is capable of accurate machining of parts which have varying hardness, complex shapes and
sharp edges that are hard to be machined by the traditional machining processes. The technology of
WEDM process is based on the conventional EDM sparking principle utilizing the widely accepted
noncontact technique of material removal [1]. The main aim of the researchers is to obtain the
improved material removal rate with desired surface finish of the workpiece. Enormous research has
been carried out to maximize the material removal rate in the past.
Prasad beri et al. proposed that EDM researchers have explored a number of ways to progress
and optimize the MRR including some unique experimental models that depart from the traditional
INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 5, May (2014), pp. 180-188
© IAEME: www.iaeme.com/ijmet.asp
Journal Impact Factor (2014): 7.5377 (Calculated by GISI)
www.jifactor.com
IJMET
© I A E M E

181
EDM sparking singularity. All the research work in this area is done to achieve more efficient MRR
coupled with decline in Tool Wear Rate (TWR) and improved surface finish. They conclude that in
order to increase the MRR and reduce TWR, best suitable dielectric is needed. They also concluded
that an optimum value of powder in dielectric is necessary to achieve the high MRR and reduced
TWR [2].
Lin and Lin, reported the use of an orthogonal array, grey relational generating, grey
relational coefficient, grey-fuzzy reasoning grade and analysis of variance to study the performance
characteristics of the WEDM machining process. The machining parameters (pulse on time, duty
factor and discharge current) with considerations of multiple responses (electrode wear ratio,
material removal rate and surface roughness) were effective. The grey-fuzzy logic approach helped
to optimize the electrical discharge machining process with multiple process responses. The process
responses such as the electrode wear ratio, material removal rate and surface roughness in the
electrical discharge machining process could be greatly improved [3].
EN 31 steel, when machined with cryogenic treated brass wire, with three process parameters
namely type of wire electrode, pulse width, and wire tension, shows a significant improvement in
Surface Roughness than the untreated wire electrode. Strong interaction is observed between type of
wire and wire tension; pulse width and wire tension [4].
For determining the optimal parametric settings, lot of work has been done in the engineering
design. But mostly all of them concentrated on a single response problem. However, the WEDM
processes are having several important performance characteristics like MRR, SR, etc. The optimal
parametric settings with to different performance characteristics are different [5].
Scott et al. have presented a formulation and solution of a multi objective optimization
problem for the selection of the best parameter settings on a WEDM machine. The measures of
performance for the model were MRR and surface quality. In that study, a factorial design model has
been used to predict the measures of performance as a function of a variety of machining parameters
[6].
Fuzhu Han et al. have found that the material removal rate is much higher when short pulse
duration is used as compared to longer pulse duration. Moreover, from the single discharge
experiments, they found that when the pulse energy was reduced to a certain value; a long pulse
duration combined with a low peak value could not produce craters on the workpiece surface any
more [7]. Kodalagara Puttanarasaiah Somashekhar et al. have presented presents the formulation and
solution of optimization of various process parameters for the selection of the best control settings on
microwire electrical discharge machining. They have found that at discharge energy of 2,645 µJ,
maximum MRR of 0.0428 mm3
/ min and an overcut value of 69 µm are observed. With the value of
discharge energy changing from 32 to 4,500 µJ, the Ra value of slot surface varied from 1.17 to
4.25 µm. After the analysis the average erosion efficiency is found to be around 27% [8]. S. S.
Mahapatra et al. have used Taguchi’s parameter design to find out the significant machining
parameters affecting the performance measures. With the help of nonlinear regression analysis they
have established the relationship between control factors and responses like MRR, SF and kerf. They
have concluded that the WEDM process parameters can be adjusted to achieve better metal removal
rate, surface finish [9]. Adeel Ikram et al. have reported the effect and optimization of selected eight
control factors on material removal rate (MRR), surface roughness and kerf in wire electrical
discharge machining (WEDM) process for tool steel D2.they have developed linear regression and
additive models for all the response factors. They have concluded that pulse on-time is the most
significant factor affecting the surface roughness, kerf and material removal rate [10]. Hari Singh el
al. have studied the effects of WEDM process parameters like pulse on time, pulse off time , gap
voltage, peak current, wire feed and wire tension; on material removal rate of hot die steel (H-11)
using one variable at a time approach. After a long series of experiments they have concluded that

182
the material removal rate increases with increase in pulse on time and peak current, while decreases
with increase in pulse off time and servo voltage [11].
2. SCHEME OF EXPERIMENTS
The experiments were performed on electronica sprint cut machine using a work piece of MS
of dimensions 25mm x 25mm of thickness 5 mm.
The selected parameters and their range for the detailed experiments are shown in Table 1.
Table 1: Process Parameters & their Range
S. No. Parameter Range Unit
1 Material of Wire Copper, Brass, Zinc Coated Brass ----
2 Current 3-7 Amp
3 Ton 110-130 Micro Sec
4 Toff 35 Micro Sec
5 Wire Tension 9 Grams
6 Servo Voltage 20 Volt
The experiments have to be designed so that we can study the effect of some selected
parameters on response characteristics of WEDM process. To study the effect of same Taguchi
parametric design methodology was adopted. The experiments were conducted using
appropriate orthogonal array (OA).
The non-linear behaviour exists among the process parameters, can only be studied if more
than two levels of the parameters are used. Therefore, each parameter was analyzed at three levels.
The selected number of process parameters and their levels are given in Table 2. For the sake of
simplification, the second order interactions among the parameters were not considered.
Each three level parameter has 2 degree of freedom (DOF = Number of levels-1), overall
mean has a degree of freedom of 1, and the total DOF required for three parameters each at three
levels is 7 =1+ [3 x (3-1)]. As per Taguchi’s method the total DOF of selected OA must be greater
than or equal to the total DOF required for the experiment. So an L9 (a standard 3-level OA) having 8
= (9-1) degree of freedom was selected for the present analysis. Standard L9 OA with the parameters
assigned by using linear graphs is given in Table 3.
Table 2: Process Parameters with Symbols and their Values at Different Levels
Symbol
Process
Parameters
Unit Level 1 Level 2 Level 3
M
Material of
Wire
---- Copper Brass
Zinc Coated
Brass
C Current A 3 5 7
T Ton Micro Sec 110 120 130

183
Table 3: Orthogonal array for L9 with responses (RAW data/ S/N Ratios)
Exp.
No.
Run
Order
Material Removal
S/N Ratio (dB)
R1 R2 R3
1 1 4.41 8.33 6.37 16.08
2 4 5.56 5.88 5.72 15.15
3 7 7.3 8.71 8.01 18.07
4 2 3.25 9.85 6.55 16.32
5 5 11.43 4.29 7.86 17.91
6 8 3.11 3.17 3.14 9.94
7 3 7.21 6.33 6.77 16.61
8 6 3.09 2.9 2.99 9.53
9 9 4.6 3.23 3.92 11.85
Total 49.96 52.69 51.33
T MR=Overall mean of MR = 5.70
3. RESULTS & DISCUSSION
The average values of %age improvement in surface roughness and S/N ratio for each
parameter at Level L1, L2 and L3 are calculated and given in Table 4. These values have been plotted
in Fig. 1, Fig. 2, and Fig. 3.
Table 4: Average values & main effects: Material Removal Rate
Process
Parameter
Level Material of Wire Current Ton
Type of Data
Raw
Data
(mg)
S/N
Ratio
(dB)
Raw
Data
(mg)
S/N
Ratio
(dB)
Raw
Data
(mg)
S/N
Ratio
(dB)
Average
Values (MR)
L1 6.70 16.12 6.56 15.16 4.17 11.55
L2 5.85 13.16 5.53 13.50 5.40 13.47
L3 4.56 12.56 5.02 13.17 7.55 16.80
Main Effects
(MR)
L2-L1 -0.85 -2.96 -1.04 -1.66 1.23 1.92
L3-L2 -1.29 -0.60 -0.51 -0.33 2.15 3.33
Difference
{( L3-L2)-( L2-L1)}
-0.44 2.36 0.53 1.33 0.92 1.42
L1, L2 & L3 represent levels 1, 2, & 3 respectively of parameters. L2-L1 is the average
main effect when the corresponding parameter changes from level 1 to level 2. L3-L2
is the main effect when the corresponding parameter changes from level 2 to level 3.

184
12.5
13.5
14.5
15.5
16.5
Cu B ZB
Material of Wire
S/NRatio(dB)
4
5
6
7
%ImprovementinMR(mg)
S/N Raw data
Fig. 1: Effect of Material of Wire on S/N Ratio & Material Removal.
The effect of the Material of Wire on S/N Ratio & Material Removal can be clearly seen in
the fig. 1. It can be seen from the figure that the Material Removal decreases directly as the Material
of Wire is changed from Copper to zinc coated brass. Highest Material Removal is obtained with
Copper wire. The decrease in Material Removal is observed when the Brass Wire is used, Material
Removal is further decreased when Zinc coated Brass Wire is used for cutting. This is due to the fact
that as the hardness of the electrode material is increased the material removal is increased. In the
present study zinc coated brass wire is the soft material and copper being the hardest from rest of
two. That’s why the material removal is higher when copper wire is used for cutting.
13
13.5
14
14.5
15
15.5
16
3 5 7
Current (A)
S/NRatio(dB)
4.8
5
5.2
5.4
5.6
5.8
6
6.2
6.4
6.6
6.8
S/N Raw data
Fig. 2: Effect of Current on S/N Ratio & Material Removal.
Another Parameter that affects the Material Removal with these parameters is Current. Fig. 2
shows the variation of Material Removal with respect to Current. Highest value of Material Removal
is observed at 3A, after that when the value of current is increased from 3A to 7A the decrease in
Material Removal is observed. Lowest value of Material Removal is shown at 7A current, as shown
in the figure 2. This is because of the fact that as the hardness of the electrode wire is decreased the
material removal is also decreased. The hardness of plain brass wire is probably less as compared to
the work piece material that’s why material removal was decreased with the increase in current. Now
as the current during the cycle is increased more deterioration of wire takes place as compared to
work piece material.

185
10
11
12
13
14
15
16
17
110 120 130
Ton(Micro Sec.)
S/NRatio(dB)
3.8
4.3
4.8
5.3
5.8
6.3
6.8
7.3
7.8
S/N Raw data
Fig. 3: Effect of Ton on S/N Ratio & Material Removal
One more parameter that was used in cutting was Ton; Figure 3 shows the variation of
material removal with respect to various values of Ton. The Figure show that the material removal is
increased directly as the value of Ton is increased. Maximum value of material removal is observed at
the third value of Ton (i.e.130 micro Sec). This is due to the fact that when the cycle time (i.e. Ton) is
increased, the current is allowed to pass through the wire electrode to work piece for an increased
time interval as a result of that the material removal is increased.
Pooled ANOVA tables for raw data and S/N Ratio data of surface roughness are shown
below in the Tables 5 and 6 respectively.
Table 5: Pooled ANOVA (Raw Data, MR)
SOURCE SS DOF V F- RATIO P%
Material of Wire 20.86 2 10.43 3.43 10.17
Current 11.14 2 5.57 1.8 3.48
Ton 52.58 2 26.29 8.64 31.98
Error 60.79 20 3.03 54.36
Total (T) 145.40 26 -- 100
Significant at 95% confidence level, Fcritical =3.55
SS- Sum of Squares, DOF- Degree of Freedom, V- Variance
Table 6: Pooled ANOVA (S/N Ratio Data, MR)
SOURCE SS DOF V F- RATIO P%
Material of Wire 21.76 2 10.88 61.76 30.05
Current 6.78 2 3.39 19.25 9.03
Ton 42.36 2 21.18 120.19 58.95
Error 0.35 2 0.18 1.98
Total (T) 71.26 8 -- 100
Significant at 95% confidence level, Fcritical =3.55
SS- Sum of Squares, DOF- Degree of Freedom, V- Variance
4. ESTIMATION OF OPTIMUM RESPONSE CHARACTERISTICS
As observed the optimum values for maximum MR are M1C1T3 for both raw and S/N data.
The mean at the optimal MR (optimal value of the response characteristic) is estimated as:
MR = TTCM 2311 −++ (1)

186
T̅ = overall mean of the response = 5.70 (Table 3)
1M = Average value of MR at the First level of Material of Wire = 6.70 (Table 6)
1C = Average value of MR at the First level of Current = 6.56 (Table 6)
3T = Average value of MR at the third level of Ton = 7.55 (Table 6)
Substituting these values in equation (1), MR = 9.41
The confidence interval of confirmation experiments (CICE) and of population (CIPOP) is calculated
by using the following equations:






+=
R
1
n
1
V)f(1,FCI
eff
eeαCE
eff
eeα
POP
n
V)f(1,F
CI =
Where, Fα (1, fe) = The F-ratio at the confidence level of (1-α) against DOF 1 and error degree of
freedom fe = 4.35 (Tabulated F value)
fe = error DOF = 20 (Table 5)
N = Total number of result = 27 (treatment = 9, repetition = 3)
R = Sample size for confirmation experiments = 3
Ve = Error variance = 3.03 (Table 5)
neff = = 3.86
So, CICE = ± 12.49
And CIPOP = ± 8.26
The 95% confirmation interval of predicted optimal range (for confirmation run of three
experiments) is:
Mean MR – CICE < MR > Mean MR + CICE
-3.09 < MR > 21.90
The 95% confirmation interval of the predicted mean is:
Mean MR – CIPOP < MR > Mean MR + CIPOP
1.14 < MR > 17.67
5. CONFORMATION EXPERIMENT
In order to validate the results obtained, three confirmation experiments have been conducted
for response characteristics of MR at the optimal levels of Material of Wire at level One (M1),
Current at level One (C1) and Ton at level Three (T3). Result of conformation experiments is shown in
table 7.

187
Table 7: Predicted Optimal Values, Confidence Intervals and Results of Confirmation Experiments
Response
Characteristics
Optimal
Process
Parameters
Predicted
Optimal
Value
Confidence Interval
95%
Actual Value (Avg.
of Confirmation
EXP.)
MR M1C1T3 9.41
CICE:-3.09<MR>21.90
CIPOP:1.14<MR>17.70
CICE: 15.86
CIPOP: 12.46
% Improvement
in Ra
M2C3T3 4.58
CICE:2.77<∆Ra >6.38
CIPOP:3.79<∆Ra >5.36
CICE: 3.90
CIPOP: 4.08
CICE – Confidence interval for the mean of the confirmation experiments
CIPOP – Confidence interval for the mean of the population
6. CONCLUSIONS
The important conclusions drawn from the present study are summarized below:
1. The effect of material of wire, on percentage improvement in Material Removal Rate (MRR) is
found to be prominent.
2. As the material of the wire is changed from copper to brass then to zinc coated brass Material
Removal Rate decreases.
3. Best result is obtained with plain copper wire with a maximum MRR of 6.7%. Also at the
maximum value of Ton, MRR is found to be 7.55 %.
4. MRR reduces gradually as the value of current in the circuit was increased.
7. REFERENCES
[1] Technological Manual of Electronica Sprincut Wire-cut Electrical Discharge Machine.
[2] Prasad Beri, Dielectric Fluid in Electro Discharge Machining, International Journal of
Manufacturing Technology, 2005.
[3] Lin, J.L., Lin, C.L., The use of grey-fuzzy logic for the optimization of the manufacturing
process, Journal of Materials Processing Technology, 160, 2004, 9–14.
[4] Kapoor J., Khamba J.S., Singh S., Effects of Cryogenic Treated Wire Electrode on the
Surface of EN31 Steel Machined by WEDM, International Journal of Surface Engineering
and Material Technology, Vol. 1, Issue 1, 2011, 43-47.
[5] M. Singh, H. Lal, R. Singh, Recent Developments in WEDM: A Review, IJRMET, Vol. 3,
Issue 2, 2013, 150-152.
[6] Scott D, Boniya S, Rajurkar KR, Analysis and optimization of parameter combination in wire
electrical discharge machining, International Journal of Production research, Vol. 29, Issue
11, 1991, 2189-2207.
[7] F. Han, J. Jiang, D. Yu, Influence of machining parameters on surface roughness in finish cut
of WEDM, The International Journal of Advanced Manufacturing Technology, Vol. 34, Issue
5-6, 2007, 538-546.
[8] K. P. Somashekhar, N. Ramachandran, J. Mathew, Material removal characteristics of
microslot (kerf) geometry in µ-WEDM on aluminum, The International Journal of Advanced
Manufacturing Technology, Vol. 51, Issue 5-8, 2010, 611-626.

188
[9] S. S. Mahapatra, A. Patnaik, Optimization of wire electrical discharge machining (WEDM)
process parameters using Taguchi method, The International Journal of Advanced
Manufacturing Technology , Vol. 34, Issue 9-10, 2007, 911-925.
[10] A. Ikram, N. A. Mufti, M. Q. Saleem, A. R. Khan, Parametric optimization for surface
roughness, kerf and MRR in wire electrical discharge machining (WEDM) using Taguchi
design of experiment, Journal of Mechanical Science and Technology, Vol. 27, Issue 7,
2013, 2133-2141.
[11] H. Singh, R. Garg, Effects of process parameters on material removal rate in WEDM, Journal
of Achievements in Materials and Manufacturing Engineering, Vol. 32, 2009, 0-74.
[12] Y.S.Sable, R.B.Patil and Dr.M.S.Kadam, “Investigation of MRR in WEDM for Wc-Co
Sintered Composite”, International Journal of Mechanical Engineering & Technology
(IJMET), Volume 4, Issue 3, 2013, pp. 349 - 358, ISSN Print: 0976 – 6340, ISSN Online:
0976 – 6359.
[13] Shalaka Kulkarni and Manikrodge, “Process Parameter Optimisation in WEDM of Hchcr
Steel using Taguhi Method and Utility Concept”, International Journal of Mechanical
Engineering & Technology (IJMET), Volume 5, Issue 1, 2014, pp. 57 - 67, ISSN Print: 0976
– 6340, ISSN Online: 0976 – 6359.
[14] S V Subrahmanyam and M. M. M. Sarcar, “WEDM Process Modeling with Data Mining
Techniques”, International Journal of Advanced Research in Engineering & Technology
(IJARET), Volume 4, Issue 7, 2013, pp. 161 - 169, ISSN Print: 0976-6480, ISSN Online:
0976-6499.
[15] S V Subrahmanyam and M. M. M. Sarcar, “Parametric Optimization for Cutting Speed – A
Statistical Regression Modeling for WEDM”, International Journal of Advanced Research in
Engineering & Technology (IJARET), Volume 4, Issue 1, 2013, pp. 142 - 150, ISSN Print:
0976-6480, ISSN Online: 0976-6499.

THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM PROCESS

Recommended

Recommended

More Related Content

What's hot

What's hot (17)

Viewers also liked

Viewers also liked (13)

Similar to THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM PROCESS

Similar to THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM PROCESS (20)

More from IAEME Publication

More from IAEME Publication (20)

Recently uploaded

Recently uploaded (20)

THE EFFECT OF DIFFERENT WIRE ELECTRODES ON THE MRR OF MS WORKPIECE USING WEDM PROCESS