Machining performance and optimization of process parameters of Incoloy alloy A-286 using EDM process

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Machining performance and optimization of process parameters of
Incoloy alloy A-286 using EDM process
Kunal
a,
⇑
, P. Sudhakar Rao
b,
⇑
, Mohd. Yunus Khan
c
, Anjaiah Madarapu
d
a
Department of Mechanical Engineering, National Institute of Technical Teachers ’ Training and Research(NITTTR), Chandigarh, India
b
Department of Mechanical Engineering, National Institute of Technical Teachers ’ Training and Research(NITTTR), Chandigarh, Indian
c
Department of Mechanical Engineering, National Institute of Technical Teachers ’ Training and Research(NITTTR), Chandigarh, India
d
Department of Mechanical Engineering, Guru Nanak Institutions Technical Campus, Ibrahimpatnam, Hyderabad, 501506 Telangana, India
article info
Article history:
Available online xxxx
Keywords:
EDM
Superalloy
MRR
Surface roughness
abstract
The major concern faced by various manufacturing Industry is to obtain the optimum machining vari-
ables maximum rate of material removed, low rate of tool wear and good value of surface integrity for
a superalloys which is desirable characteristics from machining performance. The problem identified
with the machining of the Incoloy alloy A286 which are considered very hard for getting machined which
can be attained by Electrical Discharge Machining (EDM). The presented paper shows impact in various
input parameters that is current, pulse on time and pulse off time and the output parameters that is
material removal rate (MRR), electrode wear rate (EWR) and surface roughness (SR) based on the exper-
iment performed that are thoroughly studied also during machining of Incoloy alloy A-286 workpiece
while machining it with the copper as tool electrode material. Response Surface Methodology based
Box Bekhen Design technique was used for Machining variables to get optimized. The parameters for
optimization were later calculated.
Ó2023 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the 4th International Con-
ference on Contemporary Advances in Mechanical Engineering.
1. Introduction
The various capabilities of machining have increased in the
industrial field leading to high financial growth and helped in
developing and generating applied research field interest in the
hybrid machining also [1–3]. The paper shows the experimental
investigation superalloys based on the different studies used
widely various fields including aerospace various automobile com-
ponents and materials are required to get good strength and good
resistance to wear at extremely high heated temperature [4–7].
Incoloy alloy A286 is an iron based alloys are generally used in
application in Marine and aerospace engineering areas. It is having
very high corrosion resting tendency therefore it is mostly used in
application areas such as nuclear sciences, biotechnology etc. [8–
11]. These alloys are mostly supplementing the areas of the pollut-
ing control setups. These characteristics and property lead to lesser
tool life, at time of machining as its usage is much lesser despite of
good features [12]. Thus, utilized electrode of these alloys for
machining should cause more removal of metal from workpiece.
2. Recent research status
Lee et al. [13] revealed on the Inconel 718 workpieces using
EDM machining showed lowering its fatigue life of the specimen
and went to conclusion that its fatigue life is less. Mohan et al.
[14] studied EDM process response output variable and used it in
machining of Hastelloy by EDM machine. Conditions parameters
developed relationship between input and output variables. Eyer-
cioglu et al. [15] results demonstrated that Rate of metal removal
and wear of electrode behaved non proportionally with input
variables
Rajyalakshmi [16] studied various variables and characteristics
in various hybrid machining with this type of machining. Ahmad
and Lajis [17] found out the issues in the checking of surface
roughness while machining of nickel based alloy by Electrical Dis-
charge Machining process. The surface integrity was studied spark-
ing effects on material heat released, followed by swift cooling.
Mustafa and Çaydasß[18] studied, found out features of the
https://doi.org/10.1016/j.matpr.2023.02.264
2214-7853/Ó2023 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the 4th International Conference on Contemporary Advances in Mechanical Engineering.
⇑
Corresponding authors.
E-mail addresses: kunal.mech20@nitttrchd.ac.in,psrao@nitttrchd.ac.in ( Kunal),
kunal.mech20@nitttrchd.ac.in,psrao@nitttrchd.ac.in (P. Sudhakar Rao).
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
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journal homepage: www.elsevier.com/locate/matpr
Please cite this article as: Kunal, P. Sudhakar Rao, Mohd. Yunus Khan et al., Machining performance and optimization of process parameters of Incoloy alloy
A-286 using EDM process, Materials Today: Proceedings, https://doi.org/10.1016/j.matpr.2023.02.264

machining of most effecting variables during the experimentation.
Pradhan et al. [19] performed the experiment by help of design of
experiment drawn array model applying it was performed along
with pure copper electrode with tube section; kerosene was used
as dielectric medium which was of commercial grade. Balraj
et al. [20] analysed that experiment of on EDM while using gra-
phite as electrode did very well achieving good MRR, with accept-
able relative electrode wear. Mohan et al. [21] investigated various
output variables and characteristics of nickel based alloy material
with output and input variables and studied mathematical models.
Dhanabalan et al. [22] modelled and analysed of surface finish
of machined job by Electrical Discharge Machine. Also found out
the formation of white layer after performing machining the sur-
face. Luis et al. [23] for generated the the connection of applied
variables in material and developed mathematical models for cur-
rent, duration of pulse, on the output variables, EWR and SR.
Taweel et al. [24] developed inside profile data of nickel based
alloy. Leera et al. [25] used and optimised of with high performing
features of Inconel 718 and concluded that the Taguchi Method
useful for the getting suitable values. BintiIzwan et al. [26] found
out the effects of the EDM process with composite electrode in
the nickel based alloys. Shahri et al. [27] revealed suspension of
Nickel based EDM dielectric medium machined finished surface.
The experiment investigated that type of dielectric medium takes
a significant character. Balamurugan and Gowthaman [28] showed
the ion in enhancing machine of nickel based alloy with some addi-
tions effect of graphite powder as addition and found the increased
in material Removal Rate. Sahu et al. [29] revealed that the exper-
imentation on the nickel based alloy and found out the various
optimum input variables values for getting the proper values out-
put parameters. Selvarajan et al. [30] carried out the machining
process by EDM on nickel alloy with nano tube of carbon combined
with the other fluid dielectric medium.
Singh et al. [31] showed small holes in nickel based superalloys
in experimentation using the EDM process and showed that the
influential variable are mainly current and electrode rotation
whereas depth average surface roughness is mainly by current
and pulse on-time is influenced. Nayim et al. [32] revealed process
using tube like structure copper and brass electrodes for generat-
ing holes of small sizes on aerospace alloys of titanium and nickel
based alloys with rotary electrode has much impact on the
machining output parameters. Khan et al. [33] Studied biodegrad-
able sustainable mixture solution can serve as good dielectric alter-
native medium as other dielectrics are mostly health hazardous.
Khan and Rao [34] investigated importance of thermal charac-
terization rather than the mechanical characteristics of workpieces
.Khan and Rao [35] found out hybrid machining scope and that
most widely used hybrid machining are was with amalgamation
of various process. Choudhary and Jadoun [36] explained a com-
parative experimental processing of nickel based alloy by machin-
ing it with graphite and copper materials, optimum results were
found using both the electrodes. Chinmaya et al. [37] performed
using electrode of aluminium in normal EDM process as well as a
magnetic field surrounded EDM of machining the workpiece of
Inconel 800. The effect on the output variables Electrode wear rate
were compared and evaluated, the results showed that a magnetic
field assisted EDM more efficient and produced better machined
surfaces. Kumar and Rao [38] investigated doing the processing
of the of titanium based alloy the while adding of aluminium or sil-
icon carbide in powdered form. Thakur et al. [39] revealed the var-
ious concerns regarding various input variables and its effects on
the output variables. Amorim et al. [40] investigated the impact
of various process variables on the output characteristics during
the machining and finally got optimum values.
Dzionk and Siemiatkowski [41] used titanium based superalloy
using various types of electrodes such as graphite and aluminium
electrodes. Sahu and Rao [42] performed experiment on titanium
based superalloy to measure surface integrity at various levels of
current and voltage. As current increases keeping voltage constant
Surface roughness increases. Alhodaib and Shandilya [43] per-
formed experiment on machining of titanium alloy for finding
out the thermal and electrical properties on the Electrical discharge
machining process. Khan et al. [44] investigated the various types
of biodegradable oil production and used for the dielectric fluid in
machining. the conclusion was drawn that MRR increase with the
increase in current values. The positive charge polarity has much
more impact on the MRR and SR.
3. Experimental methodology
From the pilot studies performed by using One Factor at a time
(OFAT) approach helped to give proper ranges of the variables of
input to be taken for the electrical discharge machine. Before the
beginning of the machining is performed particular layer of the
input variables is taken and their range is fixed followed by a sys-
tematic arrangement of the experimental performance. Proper
Selection of variables for input as given in Table 1 and proper fixing
of its ranges is one of the vital to obtain the suitable results. It
allows reducing the optimum number of experimental perfor-
mance to be used while the machining is done. This preliminary
experiment was used for getting further experimentation per-
formed. So, OFAT method obtain the input variables to be used
and obtain the optimum range of main experimentation The differ-
ent sheets of Incoloy alloy A 286 of dimension 70 mm*140 mm*2.
5 mm shown in Fig. 1 below is taken for pilot and experimental
work respectively .The ranges observed to be significant are as fol-
lows. The model is generated by using the 3 levels of design of
experiments based BBD technique is used to generate the Design
of Experiment. The most suitable tool copper is used as the elec-
trode .The dimension of electrode is 10 mm in diameter and
60 mm in the length shown below in Fig. 2. The evaluation of
the Surface integrity is done by using the Optical Profilometers.
In MRR and EWR are calculated by their weight difference (initial
weight-final weight). The surface roughness measuring machine
used for analyzing the surface integrity is the Taylor Hobson Three
Dimensional optical surface Profilometer. This profilometer has
been designed to provide high precision non-contact measurement
of the wholly symmetric surfaces such as lens and free form con-
tinuum surfaces. This better machining accuracy is requirement
Nomenclature
ANOVA Analysis of variance
BBD Box-Behnken Design
DOE Design of experiment
EDM Electric discharge machining
MRR Material removal rate
OFAT One factor at a time
RSM Response surface methodology
SR Surface roughness
TWR Tool wear rate
Kunal, P. Sudhakar Rao, Mohd. Yunus Khan et al. Materials Today: Proceedings xxx (xxxx) xxx
2

in the industrial based applications. Hence, used for getting the
roughness values for delicate surfaces, slopes and variable pitch
change.
3.1. Results and discussions
The experiment was performed by using copper Electrode,
Experiment was carried out by the RSM based BBD method tech-
nique. Three level of each input parameters were taken for exper-
imental performance by conducting the each run for 10 min each
and results were calculated as shown in the Table 2. After the each
experimental run the weight difference were taken for the MRR
and EWR. Each experimental run consist of the 3 level of each
parameter values be taken for the analysis and the optimisation.
3.2. Analysis and optimization of tool wear rate, material removal rate
and surface roughness
The Table 3 below shows the generated model is much of signif-
icant F-value model of 8.35. The a high F-value less value 0.53%
probability occurred due to disturbances. The model indicated that
the P-values<0.0500 is much significant. In this the terms produced
are significant, the given terms A, AB, AC, A
2
are having much value
significant model values.
The below Table 4 shows ANOVA for Rate of Material Removal is
much significant as Model F-value is 58.33 by using ANOVA .Due to
noise disturbances there is only an F-value probability of 0.01% .
The 0.0500 shows that model defined is significant for given P-
values lower . In this case most of the terms generated are signif-
icant. The given 0.1000 values represented having significant val-
ues is not achieved.
The Table 5 below shows ANOVA for MRR, the Model F-value
shows significant as value of 2.29 show in this given model . The
F-value 14.32% was due to noise, show the representation that
the 0.0500P-values generated is having significant value. The situ-
ation AB, A
2
are model significant is shown by the model terms.
The utility of 0.1000 shows the insignificant model.
3.3. Mathematical modelling and regression analysis
The following actual equation is developed while performing
the Experiment hence the Electrode wear rate is highly dependent
upon the main current then Pulse time(on/off) .
EWR=+0.025640 + 0.030350 *Current + 0.006613* T (on)
0:008013 Tðoff Þþ0:034775 Current Tðoff Þ
0:049425 TðonÞTðoff Þþ0:006450 Current2þ0:035030 Tðoff Þ2
Table 1
Ranges fixed for main experiment based on pilot experiment.
S. No. Parameter Level(-1) Level(0) Level(+1)
1. Current (A) 7.81 9.37 10.93
2. Pulse on Time (
l
s) 200 300 500
3. Pulse off Time (
l
s) 100 150 200
Fig. 1. Machined Workpiece.
Fig. 2. Tool Electrode.
Table 2
Experimental results.
Input parameters Output parameters
Std Run F1 current (A) F2
pulse on time (
l
s)
F3
Pulse off time (
l
s)
EWR (mm
3
/min) MRR (mm
3
/min) SR (
l
m)
16 1 0 0 0 32.0060 0.8501 0.4630
32 1 1 0 32.0036 0.0113 1.1588
13 3 0 0 0 32.0031 0.8513 0.6541
64 1 0 1 32.0010 1.5579 0.9491
15 5 0 0 0 31.9864 0.8501 0.4582
12 6 0 1 1 31.9841 1.1095 0.3220
10 7 0 1 1 31.9786 0.9269 0.3309
8 8 1 0 1 31.9749 3.8765 0.6932
79 1 0 1 31.9726 0.9030 0.6315
17 10 0 0 0 31.9646 0.8488 0.5675
14 11 0 0 0 31.9621 0.8438 0.4622
9120 11 31.9589 0.8093 0.6385
51310 1 31.9540 0.8811 0.6171
11 14 0 1 1 31.9513 1.9672 0.4943
4 15 1 1 0 31.9469 2.5025 0.2375
2161 1 0 31.9325 2.9206 0.7980
11711 0 31.9267 0.8405 0.5745
Kunal, P. Sudhakar Rao, Mohd. Yunus Khan et al. Materials Today: Proceedings xxx (xxxx) xxx
3

The following actual equation is developed the relationship
after performing the Experiment hence the Metal Removal rate is
highly dependent upon the main current Pulse time (on/off).
MRR=+0.873768 + 1.02770*Current-0.648425 T(on)
þ0:460125 Tðoff Þþ0:574175Current Tðoff Þ
0:243825 TðonÞTðoff Þþ0:663771Current
2
þ0:298271 Tðoff Þ
2
The following actual equation is developed the relationship
after performing the Experiment hence the Surface Roughness is
highly dependent upon the main Pulse time(on/off)
SR=+0.521000—0.038012* Current 0.057012* T(on)
0:049325 Tðoff Þ0:286200 Current TðonÞ
0:067575 Current Tðoff Þþ0:033825 TðonÞTðoff Þ
þ0:223750 Current
2
0:052550 TðonÞ
2
0:022025 Tðoff Þ
2
The Fig. 3 below show the predicted vs. actual value of Electrode
wear Rate .The graph clearly shows that actual model of Electrode
wear Rate developed is in contrast with the predicted theoritical
values generated during the experimental performance which
can be validated easily by seeing the spread of the actual values
to the predict line.
The Fig. 4 below show the manifest predicted vs. experimental
value of thethe output variable that is rate of material removal. The
graph clearly shows that actual model of Metal Removal Rate
developed is in adjecent and in close proximity with the predicted
theoritical values generated during the experimental performance
which can be validated easily by seeing the spread of the actual
values to the predicted actual line.
The Fig. 5 below reveal the predicted vs. experimental value of
theSurface integrity. This graph clearly shows that actual model of
Surface Roughness developed is in close proximity with the pre-
dicted theoritical values generated during the experimental perfor-
mance which can be validated easily by seeing the spread of the
true values to the estimated actual line.
The given Fig. 6 below shows perturbation graph of Electrode
wear Rate which will assists in comparing the influence on various
variables in a suitable point location in the design area. The output
Table 3
Analysis of variance for electrode wears rate.
Source Sum of Squares df Mean Square F-value p-value
Model 0.0303 9 0.0034 22910.71 < 0.0001 significant
A-Current 0.0077 1 0.0077 52265.79 < 0.0001
B-T(on) 0.0004 1 0.0004 2427.15 < 0.0001
C-T(off) 0.0004 1 0.0004 2612.37 < 0.0001
AC 0.0045 1 0.0045 30521.52 < 0.0001
BC 0.0099 1 0.0099 67617.57 < 0.0001
A
2
0.0002 1 0.0002 1344.18 < 0.0001
C
2
0.0052 1 0.0052 35468.91 < 0.0001
Residual 0.0004 1 0.0004 2822.82 < 0.0001
Lack of Fit 0.0011 1 0.0011 7406.42 < 0.0001
Pure Error 1.028E-06 7 1.469E-07
Cor Total 8.400E-07 3 2.800E-07 5.96 0.0587 not significant
Table 4
Analysis of variance for material removal rate.
Source Sum of Squares df Mean Square F-value p-value
Model 15.09 9 1.68 9277.49 < 0.0001 significant
A-Current 8.94 1 8.94 49465.50 < 0.0001
B-T(on) 0.5481 1 0.5481 3032.65 < 0.0001
C-T(off) 1.56 1 1.56 8624.44 < 0.0001
AC 0.0066 1 0.0066 36.66 0.0005
BC 1.31 1 1.31 7229.25 < 0.0001
A
2
0.1890 1 0.1890 1045.88 < 0.0001
C
2
2.10 1 2.10 11625.15 < 0.0001
Residual 0.0139 1 0.0139 76.88 < 0.0001
Lack of Fit 0.2978 1 0.2978 1647.83 < 0.0001 not significant
Pure Error 0.0013 7 0.0002
Cor Total 0.0005 3 0.0002 1.00 0.4785 not significant
Table 5
Analysis of variance for surface roughness.
Source Sum of Squares df Mean Square F-value p-value
Model 0.5870 9 0.0652 28.10 0.0001 significant
A-Current 0.0116 1 0.0116 4.98 0.0609
B-T(on) 0.0423 1 0.0423 18.24 0.0037
C-T(off) 0.0174 1 0.0174 7.48 0.0292
AC 0.3275 1 0.3275 141.09 < 0.0001
BC 0.0182 1 0.0182 7.86 0.0264
A
2
0.0032 1 0.0032 1.38 0.2789
C
2
0.1595 1 0.1595 68.73 < 0.0001
Residual 0.0106 1 0.0106 4.55 0.0703
Lack of Fit 0.0016 1 0.0016 0.7006 0.4302
Pure Error 0.0162 7 0.0023
Cor Total 0.0021 3 0.0007 0.1940 0.8954 not significant
Kunal, P. Sudhakar Rao, Mohd. Yunus Khan et al. Materials Today: Proceedings xxx (xxxx) xxx
4

is characterised by varying the each factor within its given posses-
sions and other fixed value factors. A sharp inclined in the charac-
teristic curve show the input parameters factor as the response is
sensitive to that output parameter factor Electrode wear Rate. As
closeness to flat line shows insensitiveness to change in that par-
ticular factor.
The given Fig. 7 below shows perturbation graph for the Mate-
rial Removal Rate which will assists in comparing the influence on
various variables in a suitable point location in the design area. The
output is characterised by varying the each factor within its given
possessions. A sharp inclined in the characteristic curve show the
input parameters factor as the response is sensitive to that output
parameter factor Material Removal Rate. As closeness to flat line
shows insensitiveness to change in that particular factor.
This plot Fig. 8 below represents perturbation graph for Rough-
ness of Surface which will assists in comparing the influence on
various variables in a suitable point location in the design area.
The output is characterised by varying the each factor within its
given limits possessions.
A sharp inclined in the characteristic curve show the input
parameters factor as the response is sensitive to that output
parameter factor Surface Roughness. As closeness to flat line shows
insensitiveness to change in that particular factor.
3.4. Multi response optimization
Multiple Response Optimization is taken out in Design Expert
13 Software .In the given model there are 3 responses variables
and there input parameters shown in the Table 6 below. The case
shows that considering each output and optimising the variables
for the optimisation of all output variables. From the optimisation
obtained from the numerical report contains tables the first table
shows the summary of the constraints used to produce the second
table of optimum solutions for the process.Table 7.
The optimum value of the various parameters during the Elec-
trical discharge machining that are obtained for the machining of
the incoloy alloy A 286 for output variables were obtained to be
at current = 10.033A ,Pulse on duration Time = 231.7
l
s, Pulse
off duration time = 72.8
l
s by machining it with help of copper
electrode.
Fig. 3. Predicted vs. actual value of Electrode wear Rate.
Fig. 4. Predicted vs. actual value Material Removal Rate.
Kunal, P. Sudhakar Rao, Mohd. Yunus Khan et al. Materials Today: Proceedings xxx (xxxx) xxx
5

4. Conclusions
The Box- Behnken Design (BBD) is employed for 3 levels for
development of mathematical models for estimating the Surface
integrity parameter of incoloy alloy A 286. A various multiple
objective optimization was employed for synthesising the out-
comes. The following conclusions are to be drawn as follows:
Influential parameters are in sequence of for output variables
are as follows working current , pulse duration on time and
off time respectively
Machining variables for copper electrode for Surface integrity
and Rate Tool wear was found to be at current 10.03 A pulse
duration on time 231.7
l
s and pulse duration off time-72.8
l
s.
Surface roughness was identified more at much high current.
The optimized value predicted for surface roughness was
0.478
l
m.
Higher value for Rate of Tool wear was produced at higher value
duration of on time of pulse and current. , Optimum value pre-
dicted for tool wear rate came out as 0.087 mm
3
/min.
At Higher value of current higher rate of material removal was
achieved, optimum value 2.619 mm
3
/min of rate material
removal was achieved.
Surface roughness increased at significantly lower current and
pulse on time values, with the optimal limit being achieved.
5. Future scope
The scope of study related to the above experiment can be
drawn as follows:
Different experimental runs can be conducted for different
dielectric fluid such as biodiesel, other biodegradable oil.
Fig. 5. Predicted vs. actual value of the Surface Roughness.
Fig. 6. Perturbation graph of Electrode wear Rate.
Kunal, P. Sudhakar Rao, Mohd. Yunus Khan et al. Materials Today: Proceedings xxx (xxxx) xxx
6

Fig. 7. Perturbation graph for the Material Removal Rate.
Fig. 8. Perturbation graph for the Surface Roughness.
Table 6
Optimization of constraints.
Parameter Goal Low Limit High Limit Lower Weight Upper Weight Importance
A:Current Within range 11 1 1 3
B:T (on) Within range 11 1 1 3
C:T (off) Within range 11 1 1 3
MRR 0.2613 3.901 1 1 4
EWR 0.0041 0.1658 1 1 3
SR (
l
m) 0.3111 0.9972 1 1 3
Table 7
Optimized results.
S. No. Current (A) Pulse on time (
l
s) Pulse off time (
l
s) EWR (mm
3
/min) MRR (mm
3
/min) SR (
l
m) Desirability
1. 1.000 0.285 1.000 3.790 0.066 0.472 0.788
Kunal, P. Sudhakar Rao, Mohd. Yunus Khan et al. Materials Today: Proceedings xxx (xxxx) xxx
7

Further other different types of electrodes can be used such as
Porous electrodes, reinforced electrodes can be used.
Behavior of machining can be studied under the magnetic field
with rotating electrode.
Other techniques such as CCD, Taguchi can be used
Recast layer thickness and Overcut can be further studied.
Data availability
Data will be made available on request.
Declaration of Competing Interest
The authors declare that they have no known competing finan-
cial interests or personal relationships that could have appeared
to influence the work reported in this paper.
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