Surface roughness and topography of EDM machining of Inconel 718.pdf

1. Surface roughness and topography of EDM machining of Inconel 718
Suhas Prakashrao Patil a
, G. Leela Prasad b
, Chappeli Sai Kiran c
, Sanjeev Kumar d
, V. Ramasamy e
,
H. Mohammed Ali f,⇑
a
Department, Mechanical Engineering, Arvind Gavali College of Engineering, Satata, Maharashtra 415015, India
b
Department of Mechanical Engineering, SRM Institute of Science and Technology, Vadapalani campus, Chennai 600026, Tamil Nadu, India
c
Department of Mechanical Engineering, CVR College of Engineering, Mangalpalli, Rangareddy 501510, Telangana, India
d
Department of Civil Engineering , Graphic Era Deemed to be University, Bell Road, Clement Town, 248002 Dehradun, Uttarakhand, India
e
Department of Mechatronics Engineering, Velammal Institute of Technology, Chennai 601204, Tamil Nadu, India
f
Department, Mechanical Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai 89, India
a r t i c l e i n f o
Article history:
Available online xxxx
Keywords:
Inconel 718
EDM
ANOVA Taguchi
Signal-to-noise ratio
a b s t r a c t
An alloy with a nickel–chromium basis known as Inconel 718 is utilised in gas turbine and jet engine
applications. The machining of the Inconel 718 is challenging because of its higher strength and hardness.
Electrical Discharge machining is the most common operation used for machining Inconel alloys because
of its higher accuracy in machining the alloy. The machining accuracy is evaluated by the surface rough-
ness of the material. In this research, the machining of Inconel 718 alloy using the electric discharge
machining is done by varying the input parameters. Taguchi design is used for varying the input param-
eters of Electrical discharge machining. The efficiency in machining is evaluated by measuring the surface
roughness of the Inconel alloy. The signal-to-noise ratio analysis optimises process parameters for excel-
lent surface roughness. The analysis of variance is used to determine the impact of each input variable on
the surface roughness. The result of this research is helpful for researchers and industries to select the
accurate process parameter for machining Inconel 718 alloy.
Ó 2023 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the International Confer-
ence on Materials and Manufacturing for Sustainable Developments – 2022.
1. Introduction
Due to their increased strength, temperature resistance, and
corrosion resistance, alloys based on nickel and chromium, such
as Inconel 718, are utilised in automotive and aerospace applica-
tions [1]. Because of its greater strength and hardness, Inconel alloy
is the subject of study on machining [2]. Tatsuya et al. studied the
machining of Inconel alloy in turning operation using the Cubic
Boron Nitride and non-polished tool [3]. They discovered that
using a cubic boron nitride cutting tool reduces tool flank wear
by 40 % more than using a non-polished cutting tool [4]. But on
the other hand, the cubie boron nitride cutting tool is costly. In
another research, the machining of Inconel alloy using the EDM
process was studied [5]. They found that machining the Inconel
using EDM improves the surface quality of the alloy. Few authors
investigated the machining of Inconel 625 alloy under various pro-
cess conditions [6–9]. The parameters are optimised to improve
surface roughness and rate of removal of material [10]. Similarly,
in another research, Inconel, 625 alloys are machined with wire
cut EDM, and the responses rate of removal of material and surface
quality are optimised for suitable process parameters [11–14]. In
another research, Inconel, 718 alloy is machined, and the surface
topography is studied at the optimal condition [15].
The operating parameters for conventional machining must be
optimised for the highest response quality. Taguchi is a reliable
approach for developing process parameters for testing. Authors
studied the wear analysis using the Taguchi optimisation tech-
nique, and the process parameter is optimised for minimising the
force due to friction and its coefficient [16–19]. In another
research, Taguchi grey relational analysis is used to optimise the
muli objectives in turning operation [20–22]. For single response
optimisation, Taguchi signal-to-noise ratio analysis is used in many
research studies to optimise the process parameter. It has three
levels of optimisation [23–25]. Further research must be done to
determine which parameter has the most effect on the reaction.
https://doi.org/10.1016/j.matpr.2023.02.444
2214-7853/Ó 2023 Elsevier Ltd. All rights reserved.
Selection and peer-review under responsibility of the scientific committee of the International Conference on Materials and Manufacturing for Sustainable Developments –
2022.
⇑ Corresponding author.
E-mail address: h.mohammed.24680@gmail.com (H. Mohammed Ali).
Materials Today: Proceedings xxx (xxxx) xxx
Contents lists available at ScienceDirect
Materials Today: Proceedings
journal homepage: www.elsevier.com/locate/matpr
Please cite this article as: S. Prakashrao Patil, G. Leela Prasad, C. Sai Kiran et al., Surface roughness and topography of EDM machining of Inconel 718, Mate-
rials Today: Proceedings, https://doi.org/10.1016/j.matpr.2023.02.444

2. Statistical tool (ANOVA) is used in many research to determine the
influence of each process parameter on the output response [26–
28].
As a result, this study investigates the machining of Inconel 718
alloy by modifying process parameters using the Taguchi optimisa-
tion technique. Signal-to-noise ratio analysis is used to identify the
optimal parameter for minimising surface roughness [29–31]. Also,
Analysis of Variance (ANOVA) is done to find the influence of each
process parameter on the surface roughness. The outcome of this
research helps to select each optimal parameter for machining
Inconel 718 alloy.
2. Methodology
The input parameters adjusted for the experiment in wire EDM
machining Inconel 718 are pulse on-time (PoN), pulse off-time
(PoT), and servo voltage (SV). These process parameters are varied
at 3 levels [32]. The output measured is surface roughness. Table 1
shows the process parameter variation and the array of designs
used in the experiment [33]. The flowchart of the approach utilised
in this study is shown in Fig. 1.
The experiment is carried out by varying the above-mentioned
process parameter. The surface roughness of the output response is
assessed using the Mitutoyo surface quality tester. Following each
trial, the sample is tagged for surface roughness measurement [34–
38]. The Taguchi signal-to-noise ratio (S/N) analysis is used to esti-
mate the best combination for reducing surface roughness [39]. Eq.
(1) shows the method for calculating the minimal surface rough-
ness using S/N ratio analysis.
Smaller the better ¼ 10 log10½m2
=n
X
ð1Þ
In the above equation, m represents the number of experi-
ments, and n is the experimental outcome.
3. Finding and analysis
Table 2 displays the results of the experiment. Equation 2 is
used to find the best combination of output responses for minimis-
ing surface roughness.
Fig. 2 depicts the mean plot of the S/N ratio analysis. According
to the mean graph, the best combination for reducing surface
roughness is pulse on-time: 120 s, pulse off-time: 30 s, and servo
voltage: 10 V.
Once again, the experiment is conducted for the optimal combi-
nation to verify the surface roughness value [40]. The result of the
experiment on the optimal combination is shown in Table 3.
Analysis of Variance (ANOVA) is used to determine the impact
of each parameter on the output response [41–43]. The statistical
results of variance analysis are displayed in Table 4.
The R square value for the ANOVA is 95.81 %, indicating that the
model is efficient. The servo voltage has a p-value of 0.293, which
is greater than the other parameters [44]. The servo voltage pro-
vided 53.17 % to the reaction, whereas PoN and PoT contributed
11.07 % and 35.73 %, respectively. As a consequence of the ANOVA
results, it is obvious that the SV is the most influential factor for
surface roughness [45–48].
Fig. 3 also depicts an interaction plot to examine the impact of
the output parameter on changing input responses. Fig. 3 illus-
trates that the surface roughness quality improves with shorter
pulse off and on times and higher servo voltage [49]. However,
raising the PoN, PoT, and SV has no discernible result on surface
unevenness quality [50–53]. The interaction plot also shows that
the servo voltage has a substantial impact on the quality of the
machined surface [54–57]. The minimum setting of input servo
voltage results in good machined surface quality. Next to the servo
voltage pulse, off-time in machining EDM is the adjacent influenc-
ing factor [58], followed by pulse on-time [59]. Hence from the
interaction plot analysis, it is clear that the minimum value of
input responses obtains the optimal surface roughness quality.
Fig. 1. Methodology of the research.
Table 1
Variation of machining inputs with L9 array.
PoN
(ls)
PoT
(ls)
SV
(V)
110 30 10
110 40 20
110 50 30
120 30 20
120 40 30
120 50 10
130 30 30
130 40 10
130 50 20
Table 2
Experimental result.
L9 array PoN
(ls)
PoT
(ls)
SV
(V)
Surface roughness
(lm)
1 110 30 10 1.491
2 110 40 20 1.769
3 110 50 30 1.717
4 120 30 20 1.545
5 120 40 30 1.647
6 120 50 10 1.497
7 130 30 30 1.775
8 130 40 10 2.041
9 130 50 20 2.269
S. Prakashrao Patil, G. Leela Prasad, C. Sai Kiran et al. Materials Today: Proceedings xxx (xxxx) xxx
2

3. 4. Conclusion
In this study, Inconel 718 alloy is used as the workpiece mate-
rial, and EDM is used for the machining process. Using the Taguchi
design, the input parameter is modified at three variables and
three levels. Signal-to-noise ratio analysis is used to determine
the best combination for machining Inconel alloy for the best sur-
face roughness. The ideal signal-to-noise ratio combinations are
PoN: 120 s, PoT: 30 s, and SV: 10 V, according to the signal-to-
noise ratio study. The analysis of variance is performed to deter-
mine the percentage of each component that affects the surface
quality.The analysis shows that the SV is the main influencing fac-
tor with 53.17 %, followed by PoT 35.73 % and PoN 11.07 %. The bet-
ter surface roughness is achieved at the lowest value setting of the
input responses, which is confirmed by the interaction plot. Fur-
ther, the influence of material removal rate on machining the
Inconel alloy with better surface quality is to be studied in the
upcoming research.
CRediT authorship contribution statement
Suhas Prakashrao Patil: Conceptualization. G. Leela Prasad: .
Chappeli Sai Kiran: Resources, Writing – review editing. Sanjeev
Kumar: Investigation. V. Ramasamy: Supervision. H. Mohammed
Ali: Validation.
Data availability
Data will be made available on request.
Table 4
ANOVA for surface quality.
Source DF Adj SS Adj MS F-Value P-value Contribution
PoN 2 0.36216 0.18108 15.27 0.061 11.07 %
PoT 2 0.09662 0.04831 4,07 0.197 35.73 %
SV 2 0.05735 0.02868 2.42 0.293 53.17 %
Error 2 0.02371 0.01186
Total 8 0.53984 0.26993 17.69 0.551
Fig. 2. The main effect plot means surface roughness.
Table 3
optimal combination for minimising surface roughness.
Optimal combination Surface roughness (lm)
PoN: 120 ls, PoT: 30 ls and SV: 10 V 1.09
S. Prakashrao Patil, G. Leela Prasad, C. Sai Kiran et al. Materials Today: Proceedings xxx (xxxx) xxx
3

4. 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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