Experimental Investigation of EN 8 Steel through Taguchi’s Technique to
optimize Cutting Rate
Tirath Singh
Assistant Professor, Department of Mechanical Engineering
Dev Bhoomi Group of Institution, Dehradun
Uttarakhand, India.
Vijay Singh Chauhan
Assistant Professor, Department of Mechanical Engineering
Dev Bhoomi Group of Institution, Dehradun
Uttarakhand, India
Yasar Khan Assistant Professor, Department of Mechanical Engineering
Dev Bhoomi Group of Institution, Dehradun
Uttarakhand, India
Rahul Chamola
Assistant Professor, Department of Mechanical Engineering
Dev Bhoomi Group of Institution, Dehradun
Uttarakhand, India
Abstract Wire Electrical discharge Machining (WEDM) has a large
range of applications that are continuing to grow day by day.
The present applications of WEDM can be seen in the
aerospace, defense, medical, electronic, dental, optical,
jewellery and automotive industries. In this study, optimum
values of cutting process parameters for EN 8 Steel using
WEDM Process are studied. The EN8 steel is extensively
used for making gear, axles, stressed pins, studs, keys,
spindles, very small components, complex or intricate
automotive and general engineering parts. It is also preferable
for heat treatment processes where enormous strength is the
main concern. EN8 steel has been selected for the machining
on WEDM machine (EURO CUT MARK II) of Electronica
Machine Tools Ltd because of its ample number of
applications in manufacturing sectors. A brass metal wire of
0.30 mm diameter is taken as a tool for cutting operation of
the workpiece specimen. The response of cutting rate (CR) for
decided process variables is considered for enhancing the
machining efficiency of the WEDM process. The Experiments
are performed using Taguchi’s L18 orthogonal array (OA)
under umpteen cutting factors of the wire tension (WT), peak
current supply (Ip) and pulse on duration (Ton). The best-
operating conditions of process parameters for performance
parameter are obtained by using the Taguchi Method (TM).
Results show that all parameters are significant and WT has
the highest effect on cutting rate (CR) followed by Ip and Ton.
Keywords: Wire EDM, Pulse on time, cutting rate, Taguchi
method (TM), ANOVA, etc.
Introduction WEDM is a nontraditional manufacturing process in which
workpiece is machined by rapid spark generation between
workpiece and electrode wire (usually brass) which is
continuously supplied throughout the entire process.
Dielectric fluid (basically de-ionized water) is used to provide
a better cutting environment. WEDM process is mainly used
for machining complex and intricate components regardless of
their hardness, chemical composition. Wire-cut EDM can cut
plates as thick as 400 mm and is used to make dies, punches
and tools from hard metals that are difficult to produce by
other machining techniques. When low residual stresses are
our basic need, wire EDM is the best alternative because tool
electrode and workpiece are kept away from each other.
Literature Review
Tosun et al. determined the effect of input characteristics (viz.
wire speed (WS), circuit voltage (SV), pulse duration,
dielectric fluid pressure) on eroded wire electrode craters size
for the wire electrode in WEDM. AISI 4140 steel was used as
workpiece materials and 0.25 mm diameter brass wire as a
tool for the analysis. It was found that increases in circuit
voltage, wire speed and pulse duration increases the size of
the crater, whereas an increase in the pressure of dielectric
flushing reduces the size of the crater. By applying a power
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 9, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
Page 199 of 203
function, the variation of wire crater size with machining
parameters was modeled. The effect of the process
characteristics on the wire crater size was established through
analysis of variance (ANOVA) [1]. Kuriakose et al. conducted experiments on titanium alloy (Ti-
6Al-4V) using Taguchi’s L18 orthogonal array (OA). Then by
using non dominated sorting genetic algorithm, the process
parameters are visualized and finally surface roughness (SR)
and cutting velocity (CV) for the titanium alloy (Ti-6Al-4V)
are optimized [2].
Tosun et al. determined optimal cutting conditions for kerf
width and material removal rate (MRR) in Wire EDM process
by using TM. Input parameters which have been chosen are
varying open WS, pulse duration, SV and dielectric fluid
pressure. The response of cutting kerf width and MRR was
determined by ANOVA method under umpteen cutting
conditions of input process parameters and for analyzing the
variation of kerf width and MRR, Regression analysis
technique was used [3].
Miller et al. determined the effect of most influencing range of
Ton and spark cycle (SC) on different materials (titanium, Nd
Fe–B magnetic metal and carbon bipolar thin plate) after
performing the cutting process in WEDM. Electrostatic and
thermal forces are used to find the failure behaviour of thin
section of the materials in WEDM [4].
Ramakrishnan et al. investigated the Wire EDM process for
Inconel 718 workpiece and used the multi-response
optimization technique and artificial neural network (ANN)
model to determine the best cutting condition for WEDM.
Experiments were conducted on Inconel 718 by 0.25 diameter
brass wire as tool electrode. Taguchi’s orthogonal array (OA)
L9 was selected and experiments were conducted under
different settings of Ton, delay time, ignition current and wire
feed speed (WF) [5].
Methods Three process parameters one at two levels (WT) and two at
three levels (Ton and Ip) are used for the present experimental
work. It is essential to use three levels of each input process
parameter for obtaining true behaviour of output characteristic
under most favorable cutting conditions, but here two levels
of wire tension have been used for the convenience of the
process. Process parameters with their most effective levels
are given in Table 1.
Table 1: Effective Levels of Process Parameters
Input Factors Process
Parameters
Effective Levels
L1 L2 L3
A Wire Tension 6 12
B Pulse on
Time
100 110 120
C Current 6 9 12
As per TM, Two levels set of WT have one degree of freedom
(DOF) and three levels set of Ton and Ip have two DOF.
Hence, total 5 DOF for all three factors is available for present
experimental work. Taguchi's L18 Standard Orthogonal Array
is given in Table 2 which is used to find the most favorable
cutting conditions of input process parameters (WT, Ton and
Ip) over the response characteristic (CR).
Table 2: Taguchi's L18 OA used for experiments
S.N. A B C
1 Level Level 1 Level 1
2 Level 1 Level 1 Level 2
3 Level 1 Level 1 Level 3
4 Level 1 Level 2 Level 1
5 Level 1 Level 2 Level 2
6 Level 1 Level 2 Level 3
7 Level 1 Level 3 Level 1
8 Level 1 Level 3 Level 2
9 Level 1 Level 3 Level 3
10 Level 2 Level 1 Level 1
11 Level 2 Level 1 Level 2
12 Level 2 Level 1 Level 3
13 Level 2 Level 2 Level 1
14 Level 2 Level 2 Level 2
15 Level 2 Level 2 Level 3
16 Level 2 Level 3 Level 1
17 Level 2 Level 3 Level 2
18 Level 2 Level 3 Level 3
Results The experimental values for CR are given in Table no. 3. 18
experiments is performed using Taguchi experimental design
technique for determining S/N ratio. Minitab 17 software is
used for the analysis, design, and plotting of data through
tables and graphs.
Table 3: Experimental values of CR by using L18 orthogonal
array
S.N. Wire
Tension Ton Current
Cutting
Rate
1 6 100 6 0.39
2 6 100 9 0.40
3 6 100 12 0.42
4 6 110 6 0.41
5 6 110 9 0.43
6 6 110 12 0.45
7 6 120 6 0.42
8 6 120 9 0.44
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Page 200 of 203
9 6 120 12 0.44
10 12 100 6 0.45
11 12 100 9 0.46
12 12 100 12 0.47
13 12 110 6 0.44
14 12 110 9 0.49
15 12 110 12 0.46
16 12 120 6 0.46
17 12 120 9 0.47
18 12 120 12 0.50
Analysis of CR for Process Parameters
For visualizing the effect of various input characteristic
parameters on the CR, experiments were executed by using
L18 OA and the average response values of CR for all Process
Parameters at each value for S/N ratio and raw data are
graphed in Fig. 1 and Fig. 2 respectively. Fig. 1 and Fig. 2
illustrate the values of CR which are increasing continuously
when we are increasing levels of factors (WT, Ton and Ip).
This is happening because discharge energy rises uniformly
with the levels of WT, Ton and Ip. It can also be seen that CR
has the lowest value at first level of WT and has the highest
value at second level of WT. Fig. 3 and Fig. 4 show very less
intersection between the input factors for CR since the
response of input factors at a given level is almost parallel
when compared with other factor’s value at the same level.
There is some interaction between Ton and Ip and Ton at the
second level.
Fig. 1: Response graph of CR for Process Parameters (Raw
Data)
Fig. 2: Response graph of CR for Process Parameters (S/N Data)
Fig. 3: Interaction Graph of CR for Process Parameters
Interactions (Raw Data)
Fig. 4: Interaction graph of CR for Process Parameters
Interactions (S/N Data)
Optimal levels selection Process Parameters
ANOVA was performed to determine the optimal levels of the
process variables for CR. Tables 4 and 5 show values of
ANOVA for the S/N and the raw statistics for CR. It is clear
from the tables that all process characteristics (WT, Ton and
Ip) are significant and all are affecting the CR tremendously.
Tables 6 and 7 are response tables which indicate the
significance of selected input factors for CR. The tables
exhibit ranks and delta statistics values, which show the
comparative importance of each and every process variable.
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Page 201 of 203
The difference between the maximum and minimum average
for each factor is called delta value. Ranks are decided on
Minitab software by analyzing delta. Maximum value of delta
is shown as rank 1, second highest as rank 2 and rank 3 to
minimum value of delta. Rank shows the significance of WT,
Ip and Ton for the CR. Delta and rank values predicted that
WT is the most significant parameter for CR and is followed
by Ip and Ton in the same order. As CR is the characteristic of
“higher the better” type, it can be visualized from Fig. 1 that
the value of WT at second level (A2), the value of Ton at third
level (B3) and the value of current at third level (C3) provide
maximum CR. Fig. 2 also shows that the same levels of the input factors (A2, B3, and C3) as the optimal factors level for
maximum CR in WEDM.
Table 4: ANOVA for CR (S/N Ratio)
Source DO
F
Seq of
sum of
squares
Adj of
sum of
squares
Adj of
mean of
squares
F P
WT 1 3.4201 3.4201 3.42011 62.8
0
0.00
0
Ton 2 0.6699 0.6699 0.33494 6.15 0.01
5
I 2 0.9832 0.9832 0.49158 9.03 0.00
4
RE 12 0.6535 0.6535 0.05446
Total 17 5.7267
Table 5: ANOVA for CR (Raw Data)
Source DO
F
Seq of
sum of
squares
Adj of
sum of
squares
Adj of
meanof
squares
F P
WT 1 0.008889 0.0088
89
0.0088
89
61.5
4
0.0
00
Ton 2 0.001678 0.0016
78
0.0008
39
5.81 0.0
17
I 2 0.002544 0.0025
44
0.0012
72
8.81 0.0
04
RE 12 0.001733 0.0017
33
0.0001
44
Total 17 0.014844
Table 6: Response Analysis Table of CR (S/N Ratio )
Level Wire
Tension
Ton I
1 -7.498 -7.319 -7.378
2 -6.626 -7.014 -6.987
3 -6.853 -6.821
Delta 0.872 0.465 0.557
Rank 1 3 2
Table-7: Response Analysis Table of CR (Raw Data) Level Wire
Tension
Ton I
1 0.4222 0.4317 0.4283
2 0.4667 0.4467 0.4483
3 0.4550 0.4567
Delta 0.0444 0.0233 0.0283
Rank 1 3 2
Validation
The best machining condition for CR is obtained at A2 (WT),
B3 (Ton) and C3 (Ip) from Fig. 1 and Table 7 which are
optimal levels of input process parameters. The expected
mean of the CR is calculated as [6].
= + + - (1)
Where,
T = mean of the CR = (R)/18 =0.44444mm/min
R = CR values (taken from third)
= CR at the second level of WT = 0.4667mm/min
= CR at the third level of Ton = 0.4550mm/min
= CR at the third level of Ip = 0.4567mm/min
Putting the values in the above equation
= 0.4667 + 0.4550 + 0.4567 – 2 (0.44444) =
0.48952 mm/min
confirmation experiments (CICE) and population (CIPOP) are
calculated at 95% confidence intervals by using the equations
2 and 3[6], [7].
= (2)
= (3)
Here = F ratio at the confidence level of (1-α) when
DOF 1 and error DOF .
= {18/ (1+5)} =3
N = Number of experiments conducted = 18
R = Performed Sample size for CE which is one
= Error variance = 0.000144 (Table 5)
= error DOF= 12 (Table 5)
(1, 12) = 4.7472 (Tabulated value of F)
= ± 0.03019, and = ± 0.01509
The expected confidence interval for confirmation
experiments is:
Mean - < < Mean +
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Page 202 of 203
0.45933 < < 0.51971
The confidence interval of the population at 95% is:
Mean – < < Mean +
0.47443< <0.50461
The best operating values of input process variables at their
selected levels are listed as follows:
WT value at second level A2 : 12 machine unit
A third level value of Ton (B3) : 120 machine unit
A third level value of Ip (C3): 12 ampere
Confirmation Experiment
For validation of results obtained, CE has been conducted for
the output performance characteristics (CC) at optimal values
of WT, Ton and Ip. CR’s final experimental value is obtained
and analyzed with the forecast value. The final experimental
and predicted results are shown in table 7.
Table 7: Predicted optimal data and Confirmation Experiment
result
Performance
Measures/
Responses
Predicted
Optimal
level
Predicted CI (
95%
Confidence
value)
Calculated
Value (CE)
Cutting Rate 0.48952
mm/min
0.45933 <
< 0.51971
0.47443 <
< 0.50461
0.51 mm/min
The values of CR obtained through CE are within the 95% of
CICE of response characteristic. It is clearly seen that these
optimal values are within the range of process variables.
Conclusion Conclusions which can be derived from the analysis of all the
data are list below.
The value of CR increases with the increasing values
of WT, Ton and current.
For CR, WT is the most significant process
parameter and it is followed by Ip and Ton.
It is clear from the figures the interaction plots are
showing very weak interactions between the WT, Ip
and Ton in affecting the CR.
References [1] Tosun, N. and Cogun, C., An investigation of wire
wear in WEDM. Journal of Materials Processing
Technology 134: 273-278, 2003.
[2] Kuriakose. S, Shunmugam, M.S., Characteristics of
wire-electro discharge machined Ti6Al4V surface.
Materials Letters, 58: 2231– 2237, 2004.
[3] Tosun, N., Cogun, C. and Tosun, G., A study on kerf
and material removal rate in wire electrical discharge
machining based on the Taguchi method. Journal of
Materials Processing Technology, 152: 316-322, 2004.
[4] Miller, S.F., Chen, C.K., Shih, A.J., Qu, J.,
Investigation of wire electrical discharge machining of
thin cross-sections and compliant mechanisms.
International Journal of Machine Tools &
Manufacture, 45: 1717–1725, 2005.
[5] Ramakrishnan R. and Moorthy K. L., Modeling and
multi-response optimization of Inconel 718 on
machining of CNC WEDM process, Journal of
materials processing technology, 207: 343–349, 2008.
[6] Ross, P.J., Taguchi techniques for quality engineering,
McGraw-Hill Book Company, New York,1988.
[7] Roy, R.K., A primer on Taguchi method, Van Nostrand
Reinhold, New York, 1990.
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