Journal of Surface Engineered Materials and Advanced Technology, 2013, 3, 295-302
http://dx.doi.org/10.4236/jsemat.2013.34040 Published Online October 2013 (http://www.scirp.org/journal/jsemat)
Experimental Investigation of the Effect of Working
Parameters on Wire Offset in Wire Electrical Discharge
Machining of Hadfield Manganese Steel
Ashok Kumar Srivastava1*, Surjya Kanta Pal2, Probir Saha3, Karabi Das4
1Centre of Excellence in Materials Science & Engineering, Department of Metallurgical Engineering, OP Jindal Institute of Technol-
ogy Raigarh, Chhattisgarh, India; 2Department of Mechanical Engineering, IIT Kharagpur, West Bengal, India; 3Department of Me-
chanical Engineering, IIT Patna, Bihar; 4Department of Metallurgical and Materials Engineering, IIT Kharagpur, West Bengal, India.
Received July 25th, 2013; revised August 20th, 2013; accepted September 15th, 2013
Copyright © 2013 Ashok Kumar Srivastava et al. This is an open access article distributed under the Creative Commons Attribution
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ABSTRACT
In this study, a series of tests have been conducted in order to investigate the machinability evaluation of austenitic
Hadfield manganese steel in the Wire Electrical Discharge Machine (WEDM). Experimental investigations have been
carried out to relate the effect of input machining parameters such as pulse on-time (Ton), pulse off-time (Toff), wire feed
(WF), and average gap voltage (V) on the wire offset in WEDM. No analytical approach gives the exact amount of off-
set required in WEDM and hence experimental study has been undertaken. In this paper, a mathematical model has
been developed to model the machinability evaluation through the response surface methodology (RSM) capable of pre-
dicting the response parameter as a function of Ton, Toff, WF and V. The samples are tested and their average prediction
error has been calculated taking the average of all the individual prediction errors. The result shows that this mathema-
tical model reflects the independent, quadratic and interactive effects of the various machining parameters on cutting
speed in WEDM process.
Keywords: Hadfield Manganese Steel; WEDM; Pulse Time; Wire Offset; Average Gap Voltage; Response Surface
Methodology
1. Introduction
Hadfield manganese steel, with a composition of Fe-
1.2%C-13%Mn, is a remarkable engineering alloy in that
it is soft and ductile in the fully austenitic phase form.
However, when deformed, it rapidly work-hardens, even
though it may suffer considerable wear from non-impact
abrasive conditions, and impacting or gouging deforma-
tion quickly causes it to work-harden [1]. This property
makes the steel very useful in applications where heavy
impact and abrasion are involved, such as within a jaw
crusher, impact hammer, rail-road crossing (frog), etc. [2].
Wire Electrical Discharge Machining (WEDM) is an
electro thermal production process in which thin single-
strand metal wire in conjunction with de-ionized water
(used to conduct electricity) cuts through metal by the
use of heat from electrical sparks [3]. WEDM is a widely
accepted and non-traditional machining process is used
to manufacture components with intricate shapes and
profiles [4]. WEDM is found to be an extremely potential
electrothermal process [5,6], since it can be used in ma-
chining of high strength and temperature resistive (HSTR),
and it is hard and difficult to machine conductive engi-
neering materials with intricate shapes. WEDM is a wide-
spread technique used in industry for high-precision ma-
chining of all types of conductive materials such as met-
als, metallic alloys, graphite, or even some ceramic ma-
terials, of any hardness [4,7,8]. Wire-EDM is capable of
producing a fine, precise, corrosion-resistance and wear-
resistance surface [9]. WEDM uses a series of voltage
pulses, usually in rectangular form, of magnitudes of up
to 400 V and those of the frequencies of the order of 5
kHz - 200 kHz, applied between the electrodes, which
are separated by a small gap, typically 10 - 100 microns
[10]. A thin 0.05 - 0.30 mm diameter wire performs as
the electrode in WEDM and the gap between the wire
and work piece is flooded with deionized water, which
acts as the dielectric. Material is eroded ahead of the
*Corresponding author.

Experimental Investigation of the Effect of Working Parameters on Wire Offset in
Wire Electrical Discharge Machining of Hadfield Manganese Steel
296
traveling wire from the work piece by a series of discrete
sparks [11].
Hadfield manganese steels are in general difficult to
machine due to their hardness and abrasive nature of re-
inforced particles. The formation of ferric oxide (Fe2O3)
makes the WEDM process very much unstable. The gen-
eration of abnormal sparks such as arcing, short circuit,
etc. leads to such instability. It is, thus, complicated to mo-
del the process by an analytical approach based on the phy-
sics of this process. Therefore, selection of optimal para-
metric combination for obtaining better cutting perform-
ance is a challenging task in WEDM while machining the
aforesaid material. And hence, any attempt to model and
optimize the process would be useful [12].
The objective of the present work is to study the effect
of different WEDM process parameters on the wire off-
set during machining Hadfield manganese steel. An off-
set is needed to make the part of the exact size while us-
ing the power settings provided for the particular mate-
rial. Dimensional deviation or wire offset arises due to
the force generated by electro-discharge during cutting
making electrode wire bend and deviate a tiny distance
[13]. No analytical approach gives the exact amount of
offset required and hence experimental study is under-
taken. An attempt is made to develop a model using the
Response Surface Methodology (RSM), correlating input
and output parameters of the process.
2. Design of Experiments
During machining, it is required to relate the input vari-
ables such as pulse on-time (Ton), pulse off-time (Toff),
wire feed (WF), and average gap voltage (V) to the wire
offset of the process by a mathematical model. RSM is a
collection of mathematical and statistical procedures that
are useful for the modeling and the analysis of problems
in which response of demand is affected by several vari-
ables and the objective is to optimize this response [14,
15]. The essential of RSM is for replacing a complex mo-
del by an approximate one based on results calculated at
various points in the design space. RSM is especially fit
for long time computation consuming problems. Gener-
ally, RSM that employs low-order polynomial functions
(2-order is implemented often) can efficiently model low-
order problems, and corresponding computation of a RS
model is fast and cheap. In addition, RSM facilitates the
understanding of engineering problems by comparing pa-
rameter coefficients and also in the elimination of unim-
portant design variables. Low-order polynomial response
surfaces are not good for highly nonlinear problems, such
as sheet metal optimization. Thus, design of experiment
(DOE) has become the determining factor for accuracy
and efficiency of RS. There were several improvements
of DOE presented in the literature [16-22].
For the most of RS, the functions for the approxima-
tions are polynomials because of simplicity, though the
functions are not limited to the polynomials. For the cas-
es of quadratic polynomials, the response surface is de-
scribed by Equation (1):
1
2
0
11 11
kk kk
j
jjjj iji
jj iji
YXX  

 
 
 j
XX
2
(1)
where k is the number of design variables. In the case of
four variables, the response surface can be expressed by
Equation (2):
011223344
222
11122 233 344 4
12 1 213 1 314 1 4
2323 2424 3434
YββXβXβXβX
βXβXβXβ
X
βXX βXXβXX
βXXβXX βXX
 
 

 
(2)
By replacements of X5 = 2
1
X
, X
6 = 2
2
X
, X
7 = 2
3
X
,
X8 = 2
4
X
, X9 = X1 X2, X10 = X1 X3, X11 = X1 X4, X12 = X2
X3, X13 = X2 X4, X14 = X3 X4, β11 = β5, β22 = β6, β33 = β7,
β44 = β8, β12 = β9, β13 = β10, β14 = β11, β23 = β12, β24 = β13,
β34 = β14, the following Equation (3) becomes a linear
regression model as follows:
0112233445
66778 89910 10
111112 1213131414
YββXβXβXβXβ5
X
βXβXβXβXβX
βXβXβXβX

  
 
 
(3)
In the case that total number of experiments is n; the
response surface can be expressed by the following ma-
trix Equations (4) and (5):
YXB


 (4)
where,
111121
221222
331323
12
1(
01
12
23
(1)1
1
1 ...
1 ...
,... ... ..,
. ..... .
. ..... .
1 ...
,
k
k
k
nnnnk
nn
kk
yxxx
yxxx
yxxx
YX
yxxx
B








 
 
 
 
 

 
 

 

 
 
 





 







 1
nn











1)k
(5)
where ε is an error vector.

Experimental Investigation of the Effect of Working Parameters on Wire Offset in
Wire Electrical Discharge Machining of Hadfield Manganese Steel
297
3. Experiential Procedure
During this study, a series of experiments were conduct-
ed on Electra Maxicut model, CNC wire-EDM machine,
manufactured by Electronica M/C Tools Limited, Pune
(India). A schematic drawing and a photograph of the ex-
perimental system is shown in Figure 1. The voltage and
current waveform during machining has also been stored
into a computer by using Hall Effect sensors (LA 55-P,
LEM) and 100 MHz digital storage oscilloscope (Agili-
ent-54624A model) with a sampling speed of 1 mega
samples/sec. In this machine there are total 10 knob posi-
tions (1 - 10) to vary peak current. It has been seen that
peak current increases with knob position.
The constant parameters were: Wire material—Brass,
Hadfield manganese steel specimens of hardness 45 - 46
HRC, Wire diameter-250 µm, Wire tension-1000 gf, Di-
electric fluid-deionized water, Up flushing rate—4 lt/min,
Down flushing rate—5 lt/min, Peak current—2 amp,
Length of cut—6 mm, Capacitor—1 µF. The coded and
actual values of each parameter used in this work are list-
ed in Table 1. From the coded form of parameters, the
corresponding actual parameters and results of output pa-
rameters are listed in Table 2.
Four input machining parameters such as pulse on-
time (Ton), pulse off-time (Toff), wire feed (Wf), average
gap voltage (V) have been varied to see their effect on
output parameter i.e. wire offset. Hadfield manganese
steel samples of 15 mm × 15 mm × 10 mm were used.
An optical microscope (Model: SDM 210, Metal Power
Pvt. Ltd., Mumbai) was used to measure the width of the
slit of the work piece formed by WEDM. Figure 2 shows
the photographs of sample after machining.
4. Results and Discussions
4.1. Regression Analysis
The analysis of variance (ANOVA) was developed by
the researchers to test hypotheses about the situations
where there are two or more means being compared.
Though initially dealing with agricultural data [23], this
methodology has been applied to a vast array of other
fields for data analysis. It is conceptually the same as re-
gression. It allows researcher to evaluate all of the mean
differences in a single hypothesis test. An F-test is any
statistical test in which the test statistic has an F-distri-
bution under the null hypothesis. It is most often used
when comparing statistical models that have been fitted
to a dataset, in order to identify the model that best fits
the population from which the data were sampled. Exact
F-tests mainly arise when the models have been fitted to
the data using least squares. The tests in an ANOVA are
based on the F-ratio: the variation due to an experimental
treatment or effect divided by the variation due to expe-
rimental error.
Controller Tool
(Upper)
Workpiece
(Lower) Oscilloscope
Personal
Computer
(a) (b)
Figure 1. Showing: (a) a schematic drawing and (b) a photograph of the experimental syste m.
Table 1. Coding of process parameters.
Level Pulse on time “Ton” (µs) Pulse off time “Toff” (µs) Wire feed “WF” (m/min) Average gap “V” (volts)
−2 6 5 2 50
−1 8 15 3 56
0 10 25 4 62
[phone]
[phone]

Experimental Investigation of the Effect of Working Parameters on Wire Offset in
Wire Electrical Discharge Machining of Hadfield Manganese Steel
298
Table 2. Design of experiments and results (actual parameters).
Exp. No Ton (µs) Toff (µs) WF (m/min) Avg GapVolt (V) Time (min) Average Kerf width (µm) Wire offset (mm)
1 12 35 3 68 5.079 325.533 0.163
2 10 5 4 62 4.269 350.75 0.175
3 8 35 3 56 3.868 345.825 0.173
4 10 25 4 62 4.295 366.908 0.183
5 10 25 4 50 3.293 355.241 0.178
6 10 25 4 62 4.369 376.075 0.188
7 14 25 4 62 4.293 374.708 0.187
8 12 35 3 56 3.652 365.65 0.183
9 10 25 4 62 4.271 372.05 0.186
10 12 15 5 68 5.171 373.891 0.187
11 10 25 6 62 4.642 363.366 0.182
12 8 15 5 68 5.298 364.408 0.182
13 10 25 4 74 7.013 388.858 0.194
14 8 35 5 68 5.894 381.633 0.191
15 10 25 2 62 4.220 367.708 0.184
16 10 25 4 62 4.243 369.8 0.185
17 12 15 5 56 3.633 360.7 0.180
18 6 25 4 62 4.539 378.533 0.189
19 12 15 3 68 5.224 389.116 0.195
20 10 25 4 62 4.469 379.333 0.190
21 12 15 3 56 3.492 371.875 0.186
22 8 15 3 68 5.302 395.933 0.198
23 8 35 3 68 5.759 388.441 0.194
24 10 25 4 62 4.365 377.858 0.189
25 10 45 4 62 4.636 360.975 0.180
26 8 15 3 56 3.702 342.425 0.171
27 8 35 5 56 4.027 364.758 0.182
28 10 25 4 62 4.391 372.616 0.186
29 12 35 5 68 5.612 369.075 0.185
30 12 35 5 56 3.781 363.183 0.182
31 8 15 5 56 3.931 358.041 0.179
ANOVA and the F-ratio test have been performed to
justify the goodness of fit of the developed mathematical
models. The calculated values of F-ratio for the lack of
fit are found to be lesser than the standard value of the
F-ratio. The second order regression models are adequate
at 95% confidence level. Hence, the developed mathe-
matical models, which correlate the various machining
parameters with wire offset, can adequately be represent-
ed through the Response Surface Methodology.
Using software “MINITAB”, the values of the regres-
sion coefficients of Equation (2) is calculated for cutting
speed and presented by Equation (6):

Experimental Investigation of the Effect of Working Parameters on Wire Offset in
Wire Electrical Discharge Machining of Hadfield Manganese Steel
299
(1) (2)
15 mm 15 mm
15 mm
15 mm
Thickness = 10 mm
Figure 2. Photographs of Hadfield manganese steel after
machining with WEDM.
1
23
22
24
14 34
2.62558 0.07573
0.000440.13193 0.00294
0.00007 0.00028
0.00097 0.00097
Cutting SpeedX
4
X
XX
XX
XX XX




(6)
The computed values of the response parameters from
the above regressions were plotted to study the influence
of the process parameters on the output variables cutting
speed and wire offset. Figure 3 shows the graph of actual
and predicted wire offset.
4.2. Effect of Working Parameters on Wire
Offset
4.2.1. Effect of Pulse on-Time (Ton) on W i re Offset
Figure 4 shows the effect of pulse on-time on the wire
offset. It has been observed that at lower gap voltage the
wire offset increases with the increase in pulse on time. It
is because that at higher Ton, the discharge energy in-
creases resulting in larger overcut which in turn increases
the value of wire offset. At higher gap voltage the situa-
tion is completely reversed.
A ct ual O f f set vs Predi ct ed O ffset
0.15
0.16
0.17
0.18
0.19
0.2
0.21
036912 15 18 21 24 27 30 33
Exp No
W ire Of f set( mm)
Actual Wire Offset
Pr edic ted Wire
Offset
Figure 3. Graph of actual and predicted wire offset.
Wire offset vs T on
0.16
0.17
0.18
0.19
0.2
0.21
0.22
56789101112131415
Ton(microsec)
Offset(mm)
V=50
V=56
V=62
V=68
V=74
Figure 4. Effect of pulse on time (Ton) on wire offset at dif-
ferent gap voltages (V). (At WF = 4 m/min and pulse off
time Toff = 25 µs).
4.2.2. Effect of Pulse Off-Time (Toff) on Wire Offset
The effect of pulse off-time on wire offset has been
shown in Figure 5. For a particular gap voltage, wire off-
set increases initially and then starts decreasing. With too
short pulse off time, there is not enough time to clear the
disintegrated particles from the gap between the elec-
trode and the work-piece. There is also not enough time
for de-ionization of the dielectric fluid. As a result arcing
occurs and wire offset increases initially. On the other
hand, at long pulse off time this phenomenon does not
occur resulting in stable machining and hence reduces
wire offset.
4.2.3. Effect of Wire Feed (WF) on Wire Offset
The effect of wire feed on wire offset is shown in Figure
6. It shows that an increase in wire feed increases wire
offset initially, then starts to decrease for a particular gap
voltage. This is due to the fact that the contact time be-
tween the wire and electrode decreases. As a result, less
number of sparks are obtained and hence less kerf width.
So wire offset tends to decrease.
4.2.4. Effect of Average Gap Voltage (V) on Wi r e
Offset
The effect of average gap voltage on the wire offset has

Experimental Investigation of the Effect of Working Parameters on Wire Offset in
Wire Electrical Discharge Machining of Hadfield Manganese Steel
300
Wire offset vs To ff
0.16
0.17
0.18
0.19
0.2
0.21
0.22
051015 20 25 30 354045 50
Toff(microsec)
Offset(mm)
V=50
V=56
V=62
V=68
V=74
Figure 5. Effect of pulse off time (Toff) on wire offset at dif-
ferent gap voltages (V). (At WF = 4 m/min and pulse on time
Ton= 10 µs).
Wire offset vs WF
0.16
0.17
0.18
0.19
0.2
0.21
0.22
1234567
WF(m/ min)
Offset(mm)
V=50
V=56
V=62
V=68
V=74
Figure 6. Effect of wire feed (WF) on wire offset at different
gap voltages (V). (At pulse off time Toff = 25 µs and pulse on
time Ton = 10 µs).
been shown in Figure 7. It has been observed that at
lower pulse on time the wire offset increases with in-
crease in gap voltage. This is due to the fact that at higher
gap voltage the gap between the wire and work piece in-
creases resulting unstable machining which in turn in-
creases the kerf width. But at higher pulse on time the
situation is completely reversed.
4.3. The Surface Plots of Wire Offset vs. Input
Parameters
Figure 8 shows interaction effect of Ton and Toff on wire
offset. Here as we increase Ton, wire offset decreases ini-
tially and then tends to decrease at higher pulse off time.
At lower pulse on time, wire offset increases a huge
amount then decreases a little, as pulse off time is in-
creased. For higher pulse on time, wire offset increases a
little then decreases by huge amount as pulse off time is
increased. Figure 9 shows interaction effect of Ton and
Average volt on wire offset. As Ton increases, wire offset
increases. Consequently on increasing average gap volt-
age, wire offset increases initially and then it decreases.
Figure 10 shows interaction effect of Ton and Wf on wire
offset. Here at lower wire feed, if Ton is increased, wire
offset decreases. But as we go to higher wire feeds, if Ton
is increased, wire offset decreases initially and then it
Wire Offset vs V
0.16
0.17
0.18
0.19
0.2
0.21
0.22
44 5056 62 6874
V(volt)
Offset(mm)
Ton=6
Ton=8
Ton=10
Ton=12
Ton=14
Figure 7. Effect of average gap voltage (V) on wire offset at
different pulse on time (Ton). (At pulse off time Toff = 25 µs
and wire feed WF = 4 m/min).
0.17
0.18
W i r e offset
7
8
910 11 12
To n
6
7
8
0.19
45
40
35
30
25
20
Toff
15
10
5
13 14 0
Figure 8. Interaction effect of Ton and Toff on wire offset.
(Hold values—WF: 4.0; Avg. volt: 62.0).
0.16
0.17
0.18
0.19
Wire offset
7
8
910 11 12
Ton
6
7
8
0.20
0.21
13 50
14
70
A
v
60
0
v
g volt
Figure 9. Interaction effect of Ton and Avg. volt on wire
offset. (Hold values—Toff: 25.0; WF: 4.0).
0.180
0.181
0.182
0.183
0.184
0.185
0
.
186
Wir e off set
7
8
910 11 12
Ton
6
7
8
9
9
0.186
0.187
0.188
0.189
13 2
14
5
4
W
3
6
W
F
Figure 10. Interaction effect of Ton and WF on wire offset.
(Hold values—Toff: 25.0; Avg. volt: 62.0).

Experimental Investigation of the Effect of Working Parameters on Wire Offset in
Wire Electrical Discharge Machining of Hadfield Manganese Steel
301
0.170
0.175
0.180
0
.
185
Wire offset
3
45
WF
2
3
0 185
0.190
0.195
50
6
7
0
A
v
60
0
v
g vol
t
Figure 11. Interaction effect of WF and Avg. volt on wire
offset. (Hold values—Ton: 10.0; Toff: 25.0).
0
0.16
510
0.17
15 20 25 30 35
W ire offset
Toff
0.18
0.19
40 2
45
5
4
W
3
6
W
F
Figure 12. Interaction effect of Toff and WF on wire offset.
(Hold values—Ton: 10.0; Avg. volt: 62.0).
starts to increase.
Figure 11 shows interaction effect of WF and Avg. V
on wire offset. In this case, as wire feed is increased, wire
offset increases initially a little and then it decreases.
Also with the increase of average gap voltage, wire offset
increases. Figure 12 shows interaction effect of Toff and
WF on wire offset. In this case, at lower wire feed, if Toff
is increased, wire offset increases a small amount then it
goes on decreasing. But at higher wire feeds, wire offset
increases a huge amount then it decreases marginally.
5. Conclusion
Response Surface Methodology (RSM) has been used to
model the process. The model is capable of predicting
the response parameters as the function of pulse on-time,
pulse off-time, wire feed and average gap voltage. Test
results demonstrate that the model is suitable for predict-
ing the response parameter very well. In the experimental
part of this work, the effect of pulse on-time, pulse off-
time, wire feed and average gap voltage on wire offset
has been investigated. It has been found that an increase
in pulse on-time provides higher wire offset; an increase
in pulse off-time provides lower wire offset; wire feed
has minor effect giving lower wire offset and lastly an in-
crease in average gap voltage causes wire offset to in-
crease.
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