experimental studies on wire edm for surface roughness and
TRANSCRIPT
Experimental studies on Wire EDM for surface roughness and kerfwidth for shape memory alloy
ASHISH GOYAL1,* and HUZEF UR RAHMAN2
1Department of Mechanical Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, Rajasthan 303 007,
India2Centre for Nanoscience and Nanotechnology, Jamia Milia Islamia, Jamia Nagar, New Delhi 110 025, India
e-mail: [email protected]
MS received 24 March 2020; revised 1 December 2020; accepted 6 July 2021
Abstract. The present experimental work was carried out on wire electrical discharge machine (WEDM) over
NiTi shape memory alloy for biomedical applications. Improving of machineability of intricate profiles in
biomaterial applications is a challenging task. The experiments were performed on WEDM by using a brass wire
of 0.25 mm diameter, as tool electrode. A range of 4 to 8 ampere of current, range of 60-120 ls of pulse on time,
range of 15-45 ls of pulse off time, range of 11-15 cm2/gm of wire tension and range of 4-8 m/min wire feed
were selected as input parameters. The influence of these parameters was observed on surface roughness and
kerf width during fabrication of rectangular slots. The discharge craters, voids, microcracks and white layer have
been observed in machined surface by scanning electron microscopy (SEM). It was observed that at higher
values of discharge energy, the recast layer thickness increases. The higher recast layer found is 15.88 at Ip = 8,
Ton = 120, Toff = 30, WT = 11, Wf = 4. The performance of responses was analysed by the response surface
methodology and artificial neural network modelling. The obtained values of 0.993 and 0.995 from ANN model
shows strong correlation between selected parameters. The obtained desirability is 0.957 that presents the
developed model and is quite significant for both responses.
Keywords. WEDM; Response surface methodology; Artificial neural network; Scanning electron microscope;
Surface roughness; Kerf width.
1. Introduction
Shape memory alloys are smart materials, that have dis-
tinguishing properties and in comparison to other alloys.
Nowadays shape memory alloys are being used in medical
applications such as dentistry, bone repair and cardiovas-
cular stents. The nitinol SMA’s are useful for biomedical
application due to their higher shape-memory strain and
good biocompatibility. It was suggested that the machining
of shape memory alloys by EDM and wire EDM process
provides high capability of complex shapes with accurate
dimensions [1]. Experiments have been performed on
Fe30Mn6Si and Fe30Mn6Si5Cr shape memory alloys by
wire EDM machine. The results show that after-machining,
shape memory alloys exhibit a good shape recovery and
slight degradation was observed on machined surface [2].
The optimum machining condition has been investigated
for Ti50Ni50-xCux shape memory alloy by wire EDM pro-
cess. The servo voltage, pulse on time, and pulse off time
found the most significant parameter during the machining.
The zinc coated electrode provides the better MRR and SR
as compared to plain brass electrode. The zinc coated tool
electrode also produces lesser defects on the machined
surface [3]. The machining of shape memory alloy is dif-
ficult by conventional machining processes. An effort has
been made to improve the MRR and SR during the
machining of on Ti50Ni40Co10 alloy. The microstructure
and microhardness characteristics were also observed. It
was also proposed that SMA have unique physical,
mechanical and bio medical properties [4]. The mechanical
behaviour of nitinol shape memory alloy in cutting process
have been reported. It was observed that very high strength
and specific heat provides large flank wear tool [5]. The
machining and material characteristic of Nitinol-60 alloy
by wire EDM process has been investigated. The results
shows that the shape recovery ability and micro hardness of
machined surface were induced as a consequence of the
recasting and formation of re-solidified layer [6]. The
properties of NiTi alloy i.e., topography, induced layer,
phase transformation, etc. have been reported. The effect of
machining parameters on responses were also reviewed. It
was concluded that non-tradition machining provides the
better surface integrity during the machining of shape
memory alloy [7]. The experimental work has been carried*For correspondence
Sådhanå (2021) 46:160 � Indian Academy of Sciences
https://doi.org/10.1007/s12046-021-01684-3Sadhana(0123456789().,-volV)FT3](0123456789().,-volV)
out on NiTi alloy by EDM process. The material charac-
teristics of the machined surface has been investigated. Liu
et al [8] explored the process capability of wire EDM
during machining of Ni50.8Ti49.2 alloy. The machining was
performed in one main cut mode followed by four trim cut
modes. Results show that different surface roughness were
observed during the different cut modes. The different
microhardness and white layer were observed during the
different cut modes. The micro EDM process has been
adopted to performed experiments on shape memory alloy.
The micro-holes were fabricated by using the multi-ob-
jective genetic algorithm optimization technique. It was
proposed that brass electrode provided that higher MRR at
the expense of tool wear and it affects the micro-holes
quality [9].
An experimental technique has been proposed to opti-
mize the process parameters by wire EDM. The cutting
efficiency and SR have been optimized by using one factor
at a time approach during machining of Ni50.89Ti49.11 alloy.
The strain hardening effect was observed due to formation
of recast layer and oxides near the machined surface [10].
In case of EDM and its variant processes, the Taguchi’s and
RSM approaches have been used by most of the research-
ers. A few researchers have attempted to use GA, ANN,
TLBO and their modified versions [11–13]. The review of
different types of optimization methodology and soft
computing techniques such as factorial design, fuzzy logic,
ANN, etc. has been done. It was proposed that these opti-
mization techniques and modelling methods are very
powerful tool in order to analyse the results [14]. Many
researchers [15, 16] have reported difficulties while per-
forming experiments on shape memory alloy by conven-
tional machining processes. The various defects such as
poor surface quality, tool wear, low dimensional accuracy,
etc. have been observed. Kulkarni et al [17] performed
experiments on NiTi alloy to investigate the effect of Ton,
Toff, and wire feed to maximize the MRR and minimize the
TWR and SR by using multi-response optimization tech-
nique. It was reported that surface characteristics depend on
magnitude of pulse on time during machining. It was
investigated that Ton and Ip have the most significant
parameters that affect surface of the shape memory alloy.
The multi-objective optimization techniques effectively
predicted the response characteristics of wire EDM process
[18, 19]. Sharma et al [20] studied on wire spark machining
of Ni55.8 by wire EDM process. It was proposed that high
discharge parametric setting developed the formation of
cracks, Globus white layers, sub surface defects on the
machined surface. A sustainable model has been developed
to analyse the performance of micro dry wire electrical
discharge machining (lDWEDM) to analyse the perfor-
mance. The one factor at time (OFAT) and design of
experiment approaches have been used to perform the
experiments. It was proposed that the compressed air pro-
vides the stable and smooth machining operation as com-
pared to dialectic fluid [21]. The shape memory alloys have
been widely used in the medical, aerospace, automobile
field due to its outstanding properties including super
elasticity (SE) and shape memory effect [22, 23].
The machining characteristics of Ni55.8 shape memory
alloys have been investigated by the design of experimental
nd NSGA-II techniques. The developed model result was in
agreement with predicted results by the NSGA-II approach
for MRR and surface roughness values. The less micro
cracks, micro pores have been found at low value of dis-
charge energy [24]. The machining of shape memory alloys
is difficult by using conventional machining processes. The
shape memory alloy has high ductility, typical stress strain
Figure 1. Fabricated slot by WEDM Process.
Table 1. Process parameters and their range.
Control Factor Unit Level 1 Level 2 Level 3
Ip Amp 4 6 8
Ton ls 60 90 120
Toff ls 15 30 45
WT cm2/gm 11 13 15
Wf m/min 4 6 8
Current = Ip, Pulse on time = Ip = pulse off time = Toff, wire ten-
sion = WT, wire feed rate = Wf
Figure 2. Surface roughness tester.
160 Page 2 of 13 Sådhanå (2021) 46:160
behaviour, low thermal conductivity, and high degree of
work hardening. The excessive tool wear, burr formation,
poor surface finish and more power consumption has been
reported during the conventional machining process
[25–27]. The nitinol SMA’s are useful for the biomedical
application due to their higher shape-memory strain and
good biocompatibility [4, 18, 29, 30]. A literature survey
reveals limited work reported to optimize wire EDM pro-
cess parameters by hybrid optimization techniques. There-
fore an attempt has been made to perform experimental
work on NiTi alloy by wire EDM process. The response
surface methodology and artificial neural network tech-
niques have been adopted to optimize the process param-
eters for the surface and kerf width.
2. Experimental details
In the present research work, wire EDM (Electronica
Spring cut 734) machine is used for the experimental
work. The pulse on time, pulse off time, current, wire
feed rate, and wire tension were selected as process
parameters. The variation in these parameters formed the
basis of investigation of the surface roughness and kerf
width. A square workpiece of 10 cm 9 10 cm 9 6 mm
was used for the fabrication of slots on NiTi alloy. Fig-
ure 1 represents the fabricated slot by the wire EDM
Figure 3. Methodology for the wire EDM process.
Figure 4. Plan for experimental work.
Sådhanå (2021) 46:160 Page 3 of 13 160
process. The surface roughness of machined specimen
was measured by Mitutoyo’s surftest (SJ-210). Table 1
shows the process parameters and their respective ranges.
The range and level of the wire EDM process parameters
have been selected based on the pilot experiments. The
figure 2 represents the surface testing equipment that has
been used for measurement of surface roughness. It is a
portable device that can measure the specimen in vertical
and horizontal display and left and right-hand data. It has
a micro card that stores the measured data. The kerf
width of machined specimen has been measured by the
optical microscope, shown in figure 4 (f). The specimens
were measured using an optical microscope (AM4815T,
Dino-lite, Taiwan). The modelling of parameters is per-
formed by the artificial neural network (ANN) method-
ology. An ANN is an advanced computational tool which
is inspired from mimicking biological neurons of the
brain. A well-designed ANN can be extensively exploi-
ted to find the solutions of complex function in different
applications where traditional statistical methods
becomes impractical.
Figure 3 indicates the methodology used for the mod-
elling and optimization of WEDM process. Figure 4
elucidates the experimental methodology. Table 2 shows
the measured and predicted values of surface roughness and
kerf width.
3. Result and discussion
3.1 Effect on kerf width
Figure 5(a) shows the interaction plots between wire ten-
sion and pulse off time. It is observed that when Toff
increases from 15 ls to 45 ls, at the same time current
increases which decreases the discharge energy and hence
the kerf width decreases. The wire tension has less effect on
kerf width during the interaction with pulse off time. Fig-
ure 5(b) denotes the interaction plots between Toff and
peak current. The pulse off time was found as the most
significant parameter in obtained graph. At the lower value
of pulse off time i.e. 15 ls, the kerf width is less. As the
value of Toff increases from 15 ls to 30 ls, more kerf
width is obtained due to the recast layer formed on
machined surface of workpiece materials. The more kerf
width is obtained at higher value of peak current. The
Table 2. Obtained values of process parameters and responses.
Run no.
Ip Ton Toff WT Wf Surface roughness
Surface roughness predicted
(ANN) Kerf width
Kerf width predicted
(ANN)
Amp ls ls cm2/gm m/min lm lm mm mm
1 4 60 15 11 4 1.73 2.42 2.67 2.56
2 4 60 15 11 6 1.90 2.32 2.59 2.59
3 4 60 15 11 8 2.01 1.98 2.6 2.60
4 4 90 30 13 4 1.79 1.97 2.57 2.54
5 4 90 30 13 6 2.12 2.13 2.5 2.55
6 4 90 30 13 8 2.22 2.29 2.55 2.56
7 4 120 45 15 4 2.22 2.21 2.34 2.51
8 4 120 45 15 6 2.11 2.24 2.44 2.50
9 4 120 45 15 8 2.24 2.15 2.39 2.49
10 6 60 30 15 4 2.58 2.71 2.57 2.5365
11 6 60 30 15 6 2.49 1.80 2.63 2.52
12 6 60 30 15 8 2.51 1.85 2.56 2.5228
13 6 90 45 11 4 2.40 2.27 2.53 2.52
14 6 90 45 11 6 2.69 2.48 2.51 2.52
15 6 90 45 11 8 2.58 2.29 2.55 2.51
16 6 120 15 13 4 2.50 2.35 2.36 2.55
17 6 120 15 13 6 2.46 2.36 2.41 2.55
18 6 120 15 13 8 2.50 2.00 2.38 2.54
19 8 60 45 13 4 2.69 2.13 2.61 2.52
20 8 60 45 13 6 2.73 2.50 2.63 2.52
21 8 60 45 13 8 2.63 2.08 2.59 2.52
22 8 90 15 15 4 2.64 2.62 2.52 2.53
23 8 90 15 15 6 2.71 2.41 2.48 2.54
24 8 90 15 15 8 2.73 2.44 2.55 2.55
25 8 120 30 11 4 2.72 1.40 2.41 2.51
26 8 120 30 11 6 2.61 1.92 2.37 2.55
27 8 120 30 11 8 2.84 2.19 2.35 2.56
160 Page 4 of 13 Sådhanå (2021) 46:160
figure 5(C) shows the interaction of wire feed and wire
tension. It indicates that as the value of wire tension is
increased from 13 cm2/gm to 15 cm2/gm, the kerf width
starts increasing. This is attributed to the dwindling
deflections of wire and their straightening. Consequently,
generated sparks remove the material and increases the kerf
10
12
14
16
18
20 10
20
30
40
50
1.5
2
2.5
3
Toff(μs)WT(cm2/gm)
Ker
f W
idth
(mm
)
4
5
6
7
8 10
20
30
40
50
2
2.5
3
Toff(μs)Ip(Amp)
Ker
f W
idth
(mm
)
10
15
20
4
6
8
102.3
2.4
2.5
2.6
2.7
WT(cm2/gm)Wf(m/min)
Ker
f W
idth
(mm
)
60
80
100
120 45
67
8
2.3
2.4
2.5
2.6
2.7
Ip(Amp)Ton(μs)
Ker
f W
idth
(mm
)
6070
8090
100110
120
10
20
30
40
50
2.3
2.4
2.5
2.6
2.7
Toff(μs)
Ton(μs)
Ker
f W
idth
(mm
)
4
6
8
10
10
20
30
40
502.3
2.4
2.5
2.6
2.7
Wf(m/min)Toff(μs)
Ker
f W
idth
(mm
)
(a)
(c) (d)
(e) (f)
(b)
Figure 5. Surface plots between process parameters and kerf width.
Sådhanå (2021) 46:160 Page 5 of 13 160
width. The significant result is obtained during the inter-
action of pulse on time and peak current on the kerf width
in figure 5(d). The pulse off time was found as the more
significant parameter. At the lower value of pulse off time
i.e. 15 ls, the obtained kerf width is less and as the value of
Toff increases from 15 ls to 30 ls kerf width increases.
This is due to the poor flushing of the debris from work-
piece material.
The figure 5(d) represents the interaction between
pulse on time and peak current. Obtained graph shows
that with the increase of IP from 4 amp. to 8 amp.,
kerf width significantly increases. The main reason
behind attributed to this is that with increase in peak
current the discharge will be more, which results into
more material being removed from the surface of the
workpiece and higher kerf width obtained. In the pre-
sent experimental investigation, at 60 ls of pulse on
time, the high value of kerf width is obtained and after
that it starts decreasing up to 2.33 mm. The optimised
magnitude of current provides the better machining
effectiveness, otherwise more surface defects will be
observed on workpiece surface due to high discharge
energy as a result of using in appropriate magnitude of
current. The figure 5(e) shows the interaction plots
between Ton and Toff and it is found that kerf width
decreases with increase of Toff. As with increase of
Ton from 60 ls to 120 ls, kerf width starts decreasing.
The probable reason for decreasing of kerf width may
be evenly distribution of the spark. From figure 5(f) it
is observed that during interaction of Toff and Wf, the
higher value of kef width is obtained at 30 ls of pulse
off time. This may be due to less time employed for
the flushing and more kerf width is obtained and the
higher value of wire feed also increases the kef width.
This may be due to the more tool is supplied to
machine at higher wire feed and more material is
consumed which result in fat machining of the mate-
rial. This may lead to higher value of kef width.
3.2 ANOVA analysis for kerf width
Table 3 presents the fit summary analysis for the kerf width.
The obtained F value 17.78 and P value is obtained less
than 0.005 for quadratic model and it is found significant.
The larger F value and smaller P value shows the developed
model is significant. From the ANOVA results, pulse on
time parameter was found as the most significant parameter
and the proposed model is found significant for kerf width.
3.3 Effect of interaction plots on surfaceroughness
The figure 6 shows the interaction of wire EDM process
parameters with the surface roughness. Figure 6(a) presents
the interaction of pulse on time and peak current. As the
value of pulse on time increase from 60 ls to 120 ls andpeak current increases from 4 amp. to 8 amp, the energy
applied is also increased and more amount of heat energy
will be generated during this period. At high energy levels,
wire tool has more load that cause wire to vibrate, thereby
increasing the surface roughness as shown in Figure. From
the response graph of figure(b) Ip and Toff, it is observed
that increase in Ip lead to the rise in SR value whereas by
increasing Toff, SR starts decreasing. From the combined
response graph, it is found that the SR increases with
increase in Ip and decrease in Toff. It is also revealing that
SR increases with the decline in Ip and increase in Toff.
Figure 6(c) represents the interaction of Wf and Toff. The
pulse of time is the significant parameter. The higher value
of pulse off time, decreases the SR, this may be due chance
of re-solidification of machined debris on workpiece sur-
face. The wire feed rate is found less significant for the
surface roughness because it shows very less effect on SR.
Figure 6(d) shows the interaction of WT and Toff. It is
observed that the surface roughness decreases as value of
wire tension increases. At initial lower value of wire ten-
sion i.e. 11 cm2/gm, higher value of SR is obtained but as
the value of wire tension is increased from 11 cm2/gm to 13
cm2/gm the lower value of SR is obtained. This may be due
to the reduction in vibrations of wire tool and the low value
of SR is observed with increase in the pulse off time from
15 ls to 45 ls. This may be due to the proper flushing
during the machining is off and debris are removed prop-
erly so low value of SR is obtained.
Figure 6(e) explains the interaction of Ton and Toff. The
Surface roughness was most significantly affected by pulse
on time and pulse off time as shown in figure. Owing to
that, with increase in pulse on time, discharge energy
increases. During every individual spark discharge, the wire
feels an impact, which acts in the reverse direction of the
discharge rate so that surface roughness increases. Fig-
ure 6(f) shows the interaction of WT and WF. This can be
attributed to the increase of wire tension minimizing the
wire bending which leads to a dynamic stable condition of
Table 3. ANOVA analysis for kerf width.
Source Sum of Squares Df Mean F-value p-value
Model 0.2327 10 0.0233 17.78 0.00
Ip 0.0015 1 0.0015 1.17 0.29
Ton 0.2222 1 0.2222 169.75 0.00
Toff 0.0009 1 0.0009 0.66 0.43
WT 0.0021 1 0.0021 1.59 0.23
Wf 0.0002 1 0.0002 0.15 0.70
AB 0.0015 1 0.0015 1.17 0.30
AE 0.0000 1 0.0000 0.01 0.94
BC 0.0009 1 0.0009 0.68 0.42
CD 0.0051 1 0.0051 3.87 0.07
DE 0.0027 1 0.0027 2.06 0.17
Residual 0.0209 16 0.0013
Total 0.2537 26
160 Page 6 of 13 Sådhanå (2021) 46:160
45
67
8
60
80
100
1201.5
2
2.5
3
Ip(amp)Ton(μs)
surf
ace
roug
hnes
s(μ
m)
45
67
8 10
20
30
40
50
1.5
2
2.5
3
Toff(μs)
Ip(Amp)
surfa
ce ro
ughn
ess(
μm)
1020
3040
50
4
5
6
7
82.2
2.3
2.4
2.5
2.6
Toff(μs)Wf(mm/min)
surf
ace r
oughness(μ
m)
10
20
30
40
50
11
12
13
14
151
2
3
4
5
Toff(μs)WT(cm2/gm)
surf
ace
roug
hnes
s(μ
m)
60
80
100
120 10
20
30
40
50
1.5
2
2.5
3
Toff(μs)Ton(μs)
surf
ace r
oughness(μ
m)
10
15
20
45
67
8
1.5
2
2.5
3
Wf(m/min)WT(cm2/gm)
surfa
ce ro
ughn
ess(
μm)
(a)
(c) (d)
(e) (f)
(b)
Figure 6. Surface plots between process parameters and surface roughness.
Sådhanå (2021) 46:160 Page 7 of 13 160
diameter and depth of the crater leading to better surface
roughness. The higher value of SR is observed when feed
rate increased from 4 m/min to 8 m/min. As wire feed rate
increases, it results in better surface roughness. It may be
due to the rapid contact of the fresh wire during the
machining. Additionally, with the increase in WF rate, the
consumption of wire and the machining cost also increase.
3.4 ANOVA analysis for surface roughness
The obtained F and P value from ANOVA methodology are
18.21 and 0.0001, respectively for surface roughness
analysis. The obtained model is found significant due to
larger F value and smaller P value. The obtained value of
the developed model is shown in table 4. The equations (1)
and (2) show the obtained regression equation model for the
surface roughness and kef width. The ANOVA table con-
cludes that the proposed model is found significant and
peak current is found as the most important parameter.
Surface Roughness ¼ �1:0961þ 0:03468
� Ipþ�0:00451� Tonþ 0:0490
� Toff þ 0:1765�WTþ 0:24649�Wf þ 0:00189
� Ip� Tonþ�0:01214
� Ip�Wf þ�0:00017� Ton� Toff þ�0:0019
� Toff �Wtþ�0:01124�WT�Wf
ð1Þ
Kerf Width ¼ 2:80608þ 0:0385� Ipþ�0:0017
� Tonþ 0:00788� Toff
þ�0:00575�WTþ�0:0491�Wf þ�0:00049
� Ip� Tonþ�0:00020
� Ip�Wf þ 3:227� Ton� Toff þ�0:00089
� Toff �WTþ 0:00375�WT �Wf
ð2Þ
3.5 Multi-response optimization: desirabilityapproach
Every machining process carries multiple response vari-
ables that may be conflicting in nature. The optimal setting
plays an important role to obtain the maximum output form
the machining condition. In the present work, surface
roughness and kerf width have been selected as ‘smaller the
better ‘response. The surface roughness and kerf width have
been optimized simultaneously by the desirability
approach. Two responses can be optimized simultaneously
which is the main advantage of the desirability approach.
As per the response smaller the better, the responses need to
be minimized to achieve the overall desirability. Figure 7
shows desirability index graphs and individual value of the
process parameter. The best conditions and comparable
results for this desirability of the combined are 0.957 which
is near to 1. That shows the developed model is found
significant for both the responses.
Figures 8 and 9 show the comparison of experimental
results and neural network prediction results for surface
roughness and kerf width, respectively. Thus, it can be
concluded that predictions are in good agreement with the
experimental results. Hence, ANN models can predict the
response for any new input process parameters with high
accuracy.
Figure 10 shows the training values for the graph by the
ANN modelling technique. The values 0.999 and 0.995
obtained for the training and validation simultaneously that
is closer to 1, confirms the strong correlation between
process parameters and response. The obtained model
shows the good agreement between experimental and ANN
predicted values. The three graphs represent training, val-
idation, testing data. The fourth graph shows a combination
of the combined three data. The optimum structure of
artificial neural network model was selected by trial and
error by varying the neurons in hidden layer. The model
developed is a feed forward BPNN with having five process
parameters with 10 hidden layer and 2 response. So, 5-10-2
is the most suitable network for current work as shown in
figure 10.
4. Material characterization
Since wire EDM is a sparking process, the outer layer of
specimen involved is affected by high temperature changes
due to heating from the deionized water. As a result of drop
in temperature, the melted material re-solidifies on the
surface, and this is known as recast layer. The thickness of
the formed recast layer decisively depends on the levels of
parameters. It is noticed that the recast layer increases with
Table 4. ANOVA analysis for surface roughness.
Source Sum of Squares Df Mean F-value P-value
Model 2.3166 10 0.2317 18.2138 0.0001
Ip 0.5499 1 0.5499 43.2349 0.0001
Ton 0.0486 1 0.0486 3.8177 0.0684
Toff 0.0715 1 0.0715 5.6212 0.0306
WT 0.0494 1 0.0494 3.8858 0.0662
Wf 0.0541 1 0.0541 4.2542 0.0558
AB 0.0226 1 0.0226 1.7741 0.2015
AE 0.0283 1 0.0283 2.2277 0.1550
BC 0.0247 1 0.0247 1.9385 0.1829
CD 0.0249 1 0.0249 1.9556 0.1811
DE 0.0243 1 0.0243 1.9091 0.1861
Residual 0.2035 16 0.0127
Total 2.5201 26
160 Page 8 of 13 Sådhanå (2021) 46:160
the increase in pulse on time. As pulse on time was
increased, more material melts from the material and recast
layer is formed. Figure 11 indicates the effect of high
discharge energy which badly affects the surface of the
machine specimen. Figure 12 presents the formation of the
recast layer on machined surface. It is because of the
Figure 7. Desirability index of surface roughness and kerf width at optimized condition.
Figure 8. Experimental and predictions values (SR).
Sådhanå (2021) 46:160 Page 9 of 13 160
Figure 9. Experimental and predictions value (kerf width).
Figure 10. Training of the process parameters and response by ANN modelling.
160 Page 10 of 13 Sådhanå (2021) 46:160
improper flushing during the machining by which 15.88 lmand 10.48 lm size recast layer is obtained (I = 8A; Ton =
120 lm; Toff = 30 lm; WT = 11 cm2/gm; WF = 4
m/min).
Figure 12 shows the obtained surface defects on the
machined surface. It is evident that at the high energy the
surface gets damaged and deep and wide craters are
obtained on the surface which is shown in the fig-
ure (Ip = 8; Ton = 120; Toff = 30; WT = 11;Wf = 6).
Figure 13 shows the surface morphology of the ANN
solutions. The surface defects such as micro-pores, and
deposited layer is observed. At every set of the process
parameters, there is some discharge energy level, so the
formation of surface defects cannot be eliminated com-
pletely. But process parameter optimization can signifi-
cantly reduce the surface morphology deterioration at a
great extent as observed and discussed in the present
research work [24, 28].
5. Conclusion
In the present experimental study, modelling and opti-
mization of wire EDM method is performed for Ni49Ti51alloy by response surface methodology and artificial
neural network methodology. The obtained optimal
solution has been correlated with the validated tests.
Also, the experimental result and predicted results have
been compared, and the results are well within the
obtained values. The following conclusions have been
drawn:
1. It was found that factor pulse on time is most
important process parameter that affect both responses.
It was also recorded that Toff and wire feed have less
important effects on surface roughness. Further, if the
value of pulse off time is low, the poor flushing is
obtained, resulting in chances of wire breakage. This
will also lead to damaged surface and poor value for
the surface roughness.
2. The high value of discharge energy increases the
recast layer thickness. The higher recast layer obtained
is 15.88 mm (Ip = 8; Ton = 120; Toff = 30; WT = 11;
Wf = 4). The deep and wide craters are also observed
during the machining at high discharge energy.
3. Higher value of discharge energy paves the way for
increasing kerf width which creates the dimensional
deviation in the profile and better dimensional accuracy
is obtained at the low value of ton and Ip. The higher
value of Toff properly flushed away the debris from the
machining zone which enhances the dimensional
accuracy.
4. Similar approach can be utilized to study the effect of
wire EDM parameters on other responses such as
material removal rate, dimensional deviation, overcut,
etc. on different materials. The detailed experimental
investigation is also required to examine the applicability
of WEDM for producing gears, spline, curved surfaces,
etc. by different multi optimization techniques.
Figure 11. Image of the machined sample during high discharge
energy.
Figure 12. Surface defects on machined material.
Figure 13. SEM micrographs of the machined sample.
Sådhanå (2021) 46:160 Page 11 of 13 160
Acknowledgement
The authors would like to thank Prof. S.A.C. Ghani, Faculty
of Mechanical Engineering, Universiti Malaysia Pahang,
Pekan, Pahang, Malaysia for guidance. The authors grate-
fully acknowledge the financial support given by the
Malaysian Ministry of Higher Education, Universiti
Malaysia Pahang (www.ump.edu.my) and UMP Automo-
tive Engineering Centre (AEC) for Fundamental Research
Grants Scheme (FRGS), RDU160135.
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