







































K. Singh et al. /Future Technology                                                                                                   May 2025| Volume 04 | Issue 02 | Pages 
22-29 

22 

 

 

 

Article 

Investigation and optimization of process 

parameters in the electrical discharge machining 

process for Inconel 660 using response surface 

methodology 
Kunal Singh1, Kishan Pal Singh1*, Mohd. Yunus Khan2 

1Department of Mechanical Engineering, Mangalayatan University, Aligarh (UP), India 
2University of Polytechnic, Aligarh Muslim University (AMU), Aligarh(UP), India 

A R T I C L E   I N F O 
 

Article history: 
Received 07 February 2025  
Received in revised form 
12 March 2025 
Accepted 27 March 2025 
 
Keywords:  
Electrical discharge machining (EDM), Superalloy, 
Material removal rate (MRR), Tool wear rate 
(TWR), Surface roughness (SR) 
 
*Corresponding author 
Email address: 
kishan.singh@mangalayatan.edu.in 
 
DOI: 10.55670/fpll.futech.4.2.3 

A B S T R A C T 
 

Based on its exceptional mechanical and thermal qualities, Inconel 660 is a high-
performance superalloy that is frequently used in marine and aerospace 
engineering. However, attaining the ideal material removal rate (MRR), tool 
wear rate (TWR), and surface roughness (SR) is severely hampered by its low 
machinability. This study uses Response Surface Methodology (RSM) based on 
the Box-Behnken Design (BBD) to examine the impacts of different process 
parameters in Electrical Discharge Machining (EDM) of Inconel 660. Statistical 
models were created to forecast performance results, and experimental trials 
were carried out to optimize machining parameters. The results show that 
whereas pulse-off time primarily affects SR, current and pulse-on time have a 
considerable impact on MRR and TWR. The adjusted parameters offer 
improved machining performance by decreasing electrode wear and enhancing 
surface morphology. These insights allow Inconel 660 and related superalloys 
to be machined more effectively.  

1. Introduction 

Despite its exceptional strength, corrosion resistance, 
and thermal stability, superalloys like Inconel 660 are 
nonetheless challenging to machine [1]. When excellent 
mechanical performance is needed, these materials are 
widely used in biomedical, maritime, and aerospace 
applications [2]. However, traditional machining is ineffective 
due to its high hardness and low heat conductivity [3, 4]. A 
practical method for treating Inconel 660 is Electrical 
Discharge Machining (EDM), which offers accurate machining 
capabilities without causing a lot of mechanical stress [5]. 
Hard-to-machine materials can be shaped using EDM, a non-
traditional machining technique that uses electrical sparks to 
dissolve the material [6]. The use of EDM on nickel-based 
superalloys has been the subject of numerous investigations, 
with an emphasis on process parameter optimization to 
provide increased SR, reduced TWR, and improved MRR. This 
work combines statistical modeling and experimental 
analysis to identify the ideal machining settings for Inconel 
660 [7]. The nickel-based alloy Inconel 660 is utilized 
primarily in the marine and aviation fields [8]. Due to its 
extreme corrosion, resting tendency has mainly been utilized 

in domains like biology and nuclear sciences [9]. Many of 
these alloys are employed to augment the areas of the 
pollution control equipment [10]. These features and 
characteristics result in a shorter tool life during machining 
because, despite its positive attributes [11], it is used less 
frequently [12]. Because of this, using an electrode tool will 
eliminate high material from the workpiece [13]. The 
increasing use of Al-SiC composites in aerospace, automotive, 
and electronic industries necessitates efficient machining 
methods [14]. EDM is an effective non-traditional machining 
process for such composites, where thermal energy erodes 
material without direct contact. However, the presence of SiC 
affects EDM performance due to its electrical conductivity and 
thermal properties [15]. Additionally, electrode rotation 
enhances flushing efficiency and improves machining 
stability [16]. This paper examines the effect of SiC content 
and electrode rotation on EDM outcomes [17]. Mustafa 
andÇaydaş [18] examined and established characteristics of 
the vast majority of the influencing factors manufacturing 
across the testing process. With the use of an array model 
constructed specifically for the experiment and a pure copper 
anode with a tube section, Pradhan et al. [19] implemented  

 

 

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K. Singh et al. /Future Technology                                                                                                   May 2025| Volume 04 | Issue 02 | Pages 22-29 

23 

 

 
 
 
 
 
 
 
 
 

the experiment. Commercial-grade paraffin had been used as 
the dielectric medium. After assessing the EDM experiment, 
Balraj et al. [20] found that utilizing graphite as the electrode 
provided excellent outcomes: good relative electrode wear 
and an adequate MRR. Utilizing input and output variables as 
well as mathematical models, Mohan et al. [21] evaluated 
several nickel-based alloy material attributes and output 
variables. Dhanabalan et al. [22] developed an electrical 
discharge machine to reproduce and examine the surface 
quality of machined merchandise. Further, they discovered 
that a white layer had grown on the surface post-surface 
machining. The relationship between relevant variables for 
the material was discovered by Luis et al. [23], who 
additionally generated mathematical frameworks for current, 
pulse length, output variables, EWR, and SR. Structural profile 
data for the alloy based on nickel was developed by Taweelet 
al. [24]. After applying and adjusting Inconel 718's those who 
perform well capabilities, Leera et al. [25] concluded that the 
Taguchi Method helped obtain the correct values. The 
implications of the EDM method with hybrid electrodes in 
nickel-based alloys were discovered. The impacts of the 
machining technique with composite anode in nickel-based 
alloys were discovered by BintiIzwan et al. [26].  

The suspension nickel-based insulator medium molded 
polished surface was made available by Shahriet al. [27]. That 
form of dielectric medium takes an extensive character, 
following the findings of the study. Balamurugan and 
Gowthaman [28] demonstrated a surge in the rate of removal 
of metal and generated the presence of ions in a compound 
based on nickel with the impact of graphite powder on it.  
Kumar et al. [29]  experimented with Electro-Discharge 
Machining of Inconel 718 Super Alloys. Selvarajan [30] 
reviewed the EDM parameter of composite material and 
industrial demand material machining. In their studies with 
the EDM approach, Shruti  [31] revealed very micro pores in 
nickel-based alloys and discovered that electrode rotation 
and current were the key contributing variables, whereas the 
on-time pulse and current primarily impacted surface 
roughness. Nayim et al. [32] presented a method that 
generates tiny crevices in titanium and nickel-based 
aerospace alloys using rotary tools and tube-like copper tools. 
Khan et al. [33] investigated the highest output parameters 
obtained for powder concentration on SR, while  K. Tripathy 
[34] examined these factors in the EDM processing of die 
steel. As the percentage with Silica Carbide particles boosted, 
the SR decreased, giving rise to a significant improvement in 
surface integrity. For instance, to enhance the material's 
surface features, Ryota et al. [35] used a mixture of chromium 
particles and kerosene-based oil as the medium during the 
EDM process. They reported that this elevated the material's 
corrosion resistance and surface texture. By processing a 
nickel-based alloy using graphite and copper materials, 
Choudhary and Jadoun [36] reported a comparative empirical 
machining method; implementing both electrodes yielded the 
best results. In addition to utilizing an aluminum electrode in 
the standard EDM process, Chinmaya et al. [37] also polished 
an Inconel 800 workpiece in an environment of a magnetic 

field on the output parameters. When the rates of electrode 
wear were compared and assessed, it became clear that the 
impact of magnetism raised the efficiency of EDM and led to 
better-looking machined surfaces.  Kumar and Rao [38] 
explored manufacturing titanium-based alloys, which include 
powdered silicon carbide or aluminum. Concerns about 
different input factors, as well as how they affect output 
variables, have been rendered transparent by Thakur et al. 
[39]. Baldin et al. [40] evaluated how parameters affected the 
machining outcomes and ultimately found the ideal values. 
Dzionk et al. [41] employed a superalloy constructed from 
titanium and several electrode types, combining graphite-
based electrodes. Sahoo et al. [42] undertook a study on a 
superalloy with a base as chromium to test surface integrity 
at different voltage and current levels. The surface roughness 
increases as the current rises while maintaining a constant 
voltage. Attempting to investigate the heating features 
associated with this machining process, Shandilya [43] 
performed the machining of titanium alloy. Mahindra and 
Deepak et al. [44] explored the impact of a powder mixture 
dielectric made up of graphite and aluminum oxide on the 
material Inconel 718. They found that applying graphite to 
kerosene oil as a dielectric raised MRR. 

2. Research objectives 

The primary objectives of this study were to investigate 
the influence of process parameters on the machinability of 
Inconel 660 and to optimize Electrical Discharge Machining 
(EDM) parameters using Response Surface Methodology 
(RSM) and Box-Behnken Design (BBD) to enhance Material 
Removal Rate (MRR), Tool Wear Rate (TWR), and Surface 
Roughness (SR). Additionally, the study aimed to develop 
predictive models that facilitate efficient machining process 
planning and validate the experimental findings through 
statistical analysis and empirical testing. 

3. Methodology 

The pilot examinations with one fixed-at-a-time planning 
helped find appropriate input variable values for this 
machining process. While initiating the experiment, a specific 
rate of supplied parameters is taken, and their range is 
defined. This is achieved by an ordered experimental 
performance. One of the most important steps for achieving 
the desired outcomes is correctly selecting the input 
variables, shown in Table 1, and fixing their ranges. It 
additionally makes it possible to utilize fewer experimental 
outputs than desirable when the machining is done. 
Subsequent experimentation was carried out using what 
came out of this descriptive experiment. Thus, utilizing the 
one fixed-at-a-time (OFAT) approach method establishes 
proper parameters to be exploited and the perfect range for 
this main experiment. The various kinds of Inconel alloy A 
286 sheets, each measuring 65 mm by 120 mm by 3 mm, are 
utilized for pilot and research purposes, respectively. The 
intervals that followed were determined to be significant. All 
three stages of parameters are applied to generate the model. 
The layout experiment is created using the Box Bekhen 
Design method. Current, pulse-on time and pulse-off time 
were among the machining parameters that were adjusted 
within predetermined ranges. The trials were effectively 
structured using a Box-Behnken Design (BBD). The 
electrode's aspects specifications are 10 mm in diameter and 
60 mm in length. Optical profilometers are employed in the 
assessment of surface integrity. The initial weight-final 
weight difference has been utilized to calculate MRR and 
EWR. The Taylor Hobson 3D optically surface profilometer is 

Abbreviations 

BBD  Box-Behnken Design 

EDM  Electrical Discharge Machining 

MRR  Material Removal Rate 

RSM  Response Surface Methodology 

SR  Surface Roughness 

TWR  Tool Wear Rate 

 

 

 

 

 

 

 



K. Singh et al. /Future Technology                                                                                                   May 2025| Volume 04 | Issue 02 | Pages 22-29 

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the tool used to determine surface roughness and assess 
surface integrity. This profilometer intends to measure fully 
symmetric surfaces, including lenses and free-form 
continuum surfaces, with great accuracy without requiring 
contact. Improved precision is a need for various applications. 
Thus, it is employed to obtain the roughness measurements 
for uneven terrain, sloping surfaces, and changeable pitch. 

Table 1. Set values  for the conduction of the experiment based on 
the Pilot Run 

S.No. Parameter Level(-1) Level(0) Level(+1) 

1. Current  7.60 9.25 10.78 

2. Pulse on 

Time  

250 350 550 

3. Pulse off 

Time  

150 200 250 

 

 

4. Results and discussion 

The experiment employed the RSM-based BBD  (Box 
Bekhen Design) approach method and employed copper 
electrodes. Three inputs of each input parameter for the 
experimental performance were taken, and each experiment 
lasted ten minutes. After computation, the outcomes are 
shown in Table 2.  In every conducted experiment, the weight 
differences were calculated. There are three stages to each 
experimental run, and at each level, various parameter values 
are obtained for optimization and analysis. The response 
variables MRR, TWR, and SR were measured throughout 17 
experimental runs. Analysis of Variance (ANOVA) was used to 
assess the statistical significance of each parameter's impact 
on the gathered data. 

 
Table 2. Experimental Output 

  Input parameters Output parameters 

Std Run F1 
current 

F2 
 
pulse 
on 
time 

F3 
Pulse 
off 
time 

EWR MRR SR 

16 1 9.25 350 200 21.0062 0.7503 0.4623 

3 2 7.60 550 200 21.0038 0.0115 1.0575 

13 3 9.25 350 200 21.0063 0.7505 0.3652 

6 4 10.78 350 150 21.0012 1.5581 0.8487 

15 5 9.25 350 200 20.9866 0.7503 0.3565 

12 6 9.25 550 250 20.9843 1.1008 0.4254 

10 7 9.25 550 150 20.9788 0.8258 0.3321 

8 8 10.78 350 250 20.9751 3.7768 0.6921 

7 9 7.60 350 250 20.9728 0.8032 0.6325 

17 10 9.25 350 200 20.9648 0.7490 0.5656 

14 11 9.25 0350 200 20.9623 0.7440 0.4674 

9 12 9.25 250 150 20.9591 0.7095 0.6385 

5 13 7.60 350 150 20.9542 0.7811 0.6151 

11 14 9.25 250 250 20.9515 1.8674 0.4978 

4 15 10.78 550 200 20.9471 2.4023 0.2384 

2 16 10.78 250 200 20.9327 2.8208 0.7985 

1 17 7.60 250 200 20.9269 0.7407 0.5756 

 

4.1 Evaluation and  optimization of TWR, MRR, SR 
Table 3 below demonstrates how much of a significant 

8.25 F-value model was developed for the generated model. 
Disturbances had more value of F- less value of 0.41% 
probability of occurring. According to the model, P- lower 
than 0.052 values are significant. The generated sources are 
significant; the terms A, AB, AC, and A² have very valuable 
model values. The ANOVA results indicate that current is the 
most significant factor for MRR (p < 0.01), while TWR is 
significantly influenced by the interaction between current 
and pulse-off time. The model demonstrated strong 
predictive accuracy, with an R² value exceeding 90% for all 
response variables. Table 4 indicates how significant the 
ANOVA (analysis of variance) is for the Rate of Material 
Removal, with a Model F-value of 57.33 when utilizing 
ANOVA. The probability is less than 0.01%  of an F-value 
disruption due to noise. The defined model is significant for 
the supplied P-values lower, as indicated by the 0.0500. In this 
instance, the majority of the terms produced are essential. It 
is impossible to reach the 0.1000 values indicated as having 
significant values. 

Table 3.  ANOVA for electrode wear rate 

Source 
Sum of 

Squares 
df 

Mean 
Square 

F-
value 

p-value  

Model 0.0303 9 0.0025 8.25 0.0043 significant 
A-
Current 

0.0064 1 0.0085 17.48 0.0039  

B-T(on) 0.0013 1 0.0014 0.7688 0.3756  
C-T(off) 0.0015 1 0.0016 0.27 0.2862  
AC 0.0057 1 0.0059 11.00 0.0116  
BC 0.0087 1 0.0088 34.54 0.0020  
A² 0.0001 1 0.0013 0.5136 0.6311  
C² 0.0041 1 0.0063 11.61 0.0087  
Residual 0.0005 1 0.0015 0.8538 0.3659  
Lack of 
Fit 

0.0022 1 0.0023 3.85 0.1478  

Pure 
Error 

0.0024 7 0.0006    

Cor Total 0.0039 3 0.0010 234.13 <0.0001 significant 

 
 

Table 4.  ANOVA for material removal rate 
 

 

 

Source 
Sum of 

Squares 
df 

Mean 
Square 

F-
value 

p-value  

Model 13.62 7 1.19 57.33 < 0.0001 significant 

A-
Current 

8.26 1 7.39 238.45 0.0082  

B-T(on) 0.4832 1 0.4832 12.57 0.0051  

C-T(off) 1.96 1 1.58 48.50 0.0065  

AC 1.24 1 1.31 36.17 0.0003  

BC 0.2256 1 0.2368 8.86 0.0295  

A² 1.54 1 1.84 51.29 < 0.0001  

C² 0.3875 1 0.3658 10.58 0.0098  

Residual 0.3305 9 0.0425    

Lack of 
Fit 

0.0003 5 0.00005 3.09 <0.0001  

Pure 
Error 

0.0000 4 
7.557E-

06 
   

Cor 
Total 

15.65 16     



K. Singh et al. /Future Technology                                                                                                   May 2025| Volume 04 | Issue 02 | Pages 22-29 

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Table 5 displays the ANOVA for MRR; the model F-value, 
2.29 in this particular model, indicates significance. Noise 
caused the F-value of 14.22%, which indicates that the 0.0555 
P-values produced had significant values. The model terms 
demonstrate that the conditions AB and A2 are important. 
The significant model is illustrated by the utility of 0.1422. 

Table 5. ANOVA for surface roughness 

Source 
Sum of 

Squares 
df 

Mean 
Square 

F-
value 

p-
value 

 

Model 0.6150 9 0.0689 2.30 0.1422 
not 
significant 

A-
Current 

0.0127 1 0.0114 0.3845 0.0555  

B-T(on) 0.0254 1 0.0256 0.8589 0.3842  

C-T(off) 0.0199 1 0.0185 0.6428 0.4854  

AC 0.3158 1 0.3276 10.81 0.0125  

BC 0.0182 1 0.0183 0.6024 0.4532  

A² 0.0039 1 0.0046 0.1509 0.7196  

C² 0.2118 1 0.2108 6.95 0.0321  

Residual 0.0106 1 0.0116 0.3835 0.5525  

Lack of 
Fit 

0.0018 1 0.0020 0.0674 0.8015  

Pure 
Error 

0.2021 7 0.0303    

Cor 
Total 

0.1718  3 0.0605 7.90 0.0345 significant 

 

4.2  Mathematical expression and regression analysis  
The actual expression developed for the conducted 

experiment for the EWR  is: 

EWR=+0.035740+0.040450*Current+0.007723*T(on)-
0.007023* T(off)+0.035875* Current * T(off)-0.025 *T(on) * 
T(off)+0.006450* Current²+0.035030 *T(off)²                            (1) 

The actual expression developed for the conducted 
experiment for the MRR is: 

MRR=+0.863752+1.01870*Current-0.656425 T(on)+0.454125 
T(off)+0.564185Current*T(off)-0.245625T(on)*T(off)+ 
0.665771 Current² + 0.458271 T(off)²                                         (2) 

The actual expression developed for the conducted 
Experiment for the SR is: 

SR=+0.631000-0.042012* Current -0.054012* T(on)-0.049725* 
T(off)-0.275200* Current * T(on)-0.064575                              (3) 

Higher current increases MRR but also EWR and can 
worsen SR at high values. Increasing pulse-on time decreases 
MRR and improves SR but has a minimal effect on EWR. Pulse-
off time improves MRR and reduces SR but has a slight 
negative impact on EWR. Interaction effects are crucial in 
determining machining performance, meaning optimal EDM 
settings require balancing these parameters. Figure 1  
displays the expected and actual values of the electrode wear 
rate. The graph demonstrates that the theoretical values 
created during the experimental performance are consistent 
and can be readily verified by observing the plot of the actual 
values to the predicted values. Since the points are well-

aligned with the line, your model has a strong predictive 
capability with minimal deviation. If there were significant 
deviations, it would suggest model errors or areas where 
predictions need refinement. 
 

 
Figure 1. Predicted vs. actual graph for TWR, validating model 
accuracy 

The output variable, the material removal rate, is shown 
by the manifest predicted value versus the experimental 
value in Figure 2. It validates that the actual model of MRR 
generated is adjacent and near to the expected theoretical 
solutions developed at the conduction of the experimental 
procedure, as the graph clearly demonstrates.  

The expected & experimental results of surface integrity 
are shown in Figure 3. From the graph genuine to the 
estimated value actual line,  validated in the actual model of 
SR. It is established in close proximity, as shown in the graph.  
Since the points closely follow the diagonal, the model has 
minimal deviation and substantial predictive accuracy.  Any 
major deviations from the line would indicate prediction 
errors, but none are visible here. 

 
Figure 2. MRR model validation through predicted vs. actual values 

 
 
 
 
 
 



K. Singh et al. /Future Technology                                                                                                   May 2025| Volume 04 | Issue 02 | Pages 22-29 

26 

 

 
Figure 3. SR analysis, demonstrating consistency between 
experimental and predicted values 

Figure 4 illustrates the EWR perturbation plot, which will 
bolster the impact of additional parameters in the design. The 
output parameter is displayed as fluctuating between each 
value and the other value components. The characteristic plot 
displayed a steep inclination, indicating that the input 
variables were very responsive to the output parameter 
component, Electrode wear Rate. Plotline proximity indicates 
reduced sensitivity to changes in that specific factor. This 
suggests that EWR increases significantly when factor A 
increases, meaning Factor A strongly influences electrode 
wear. 

The perturbation plot of the MRR in Figure 5  illustrates 
how various design parameters may have an impact. The 
output parameter is displayed as fluctuating between all 
values and additional value components. The characteristic 
plot displayed a steep inclination, indicating that the input 
variables were very responsive to the output parameter 
component, Electrode wear Rate. Plotline proximity indicates 
reduced sensitivity to changes in that specific factor. 

 
Figure 4. Perturbation plots showing the influence of different 
machining parameters of EWR 

The SR perturbation plot in Figure 6  illustrates which 
will bolster the impact of additional parameters in the design. 
The output parameter is displayed as fluctuating between all 
values that it possesses together with additional value 
components. The characteristic plot displayed a steep 
inclination, indicating that the input variables were very 
responsive to the output parameter component, Electrode 
wear Rate. Plotline proximity indicates reduced sensitivity to 
changes in that specific factor. 

 

 
Figure 5. Perturbation plots showing the influence of different 
machining parameters for the MRR 

 

 
Figure 6. Perturbation plots show the influence of different 
machining parameters on the SR 

4.3 Multi-response optimization 
By optimizing the Design Expert software, the output and 

input factors are displayed in Table 6 according to the model 
being used. This instance emphasizes the necessity of 
considering each output and maximizing the variables to get 



K. Singh et al. /Future Technology                                                                                                   May 2025| Volume 04 | Issue 02 | Pages 22-29 

27 

 

the most out of all output variables. Tables from the numerical 
report's modification have been included; the first table 
provides an overview of limitations taken into account to 
generate the second table, including lists of the process's ideal 
responses. By employing a copper electrode to machine the 
Inconel alloy A 286, the ideal values for each of the 
parameters when the electrical discharge process of 
machining was found to be current = 11.233A, pulse on 
duration time = 220.65μs, and pulse off duration time = 
70.9μs for the output variables. 

 The optimized results in Table 7 show that a lower EWR 
is desirable as it prolongs the tool electrode’s life. The given 
value indicates minimal electrode degradation. A high MRR is 
generally preferred in machining to improve productivity. 
The optimized parameters ensure an effective balance 
between high MRR and low wear. A lower SR indicates a 
smoother surface finish. The achieved roughness is relatively 
low, which is beneficial for applications requiring high 
precision. The machined workpiece is shown in Figure 7.  

The figure shows an Electrical Discharge Machining 
(EDM) workpiece with several machined craters or cavities. 
Variations in parameters cause surface roughness (SR) 
changes, with some machined places appearing smoother and 
more uniform and others seeming rougher and darker. It 
appears that certain parameters led to larger material 
removal rates (MRR) than others, based on the contrast 
between the crater diameters and depths. A higher electrode 
wear rate (EWR), which may be impacted by excessive 
current or ineffective debris flushing or carbon deposition, 
may cause certain craters' darker appearance. Different 
machining circumstances were tried, potentially adjusting 
factors including current, pulse-on time, and pulse-off time, as 
indicated by the numbered markings next to each machined 
region. 

Table 6. Optimization of parameters 

Parameter Goal 
Low 

Limit 

High 

Limit 

Lower 

Weight 

Upper 

Weight 
Importance 

A: 

Current 

Within 

range 
-1 1 1 1 3 

B:T(on) 
Within 

range 
-1 1 1 1 2 

C:T(off) 
Within 

range 
-1 1 1 1 2 

MRR  0.0123 3.8895 1 1 3 

EWR  0.0061 0.165 1 1 3 

SR  0.2387 1.2658 1 1 2 

 

 

Table 7. Optimized results 

 

 
 
 
 
 

 
Figure 7. Machined workpiece image, providing visual confirmation 
of the optimized parameters 

5. Conclusion  

To overcome the difficulties brought on by Inconel 660's 
poor machinability, this study effectively adjusted the EDM 
process parameters for machining it. While pulse-off time is 
important in SR, current and pulse-on time have a major 
impact on MRR and TWR. The regression models that were 
created offer reliable forecasts for machining results. 
Optimized settings improve surface smoothness, decrease 
electrode wear, and increase machining efficiency. These 
findings provide important insights for industrial 
applications and advance our understanding of EDM 
processing for superalloys. For even more machinability and 
energy efficiency gains, future studies should investigate the 
incorporation of hybrid EDM techniques. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

 

 

 

 

 

 

 

 

 

 

S.No. Current(A) Pulse on 

time(μs) 

Pulse off time(μs) EWR(mm3/min) MRR(mm3/min) SR(μm) Desirability 

1. 11.233A   220.65μs 70.9μs 0.077 2.523 0.4678μm 1 



K. Singh et al. /Future Technology                                                                                                   May 2025| Volume 04 | Issue 02 | Pages 22-29 

28 

 

Conflict of interest 

The authors declare no potential conflict of interest. 

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