This paper reviews various research efforts focused on the modeling and optimization of electrical discharge machining (EDM) process parameters, highlighting input factors that affect outcomes such as material removal rate (MRR), tool wear rate (TWR), and surface roughness (SR). It details different modeling techniques and experimental studies conducted on a variety of materials and electrode types, examining the influence of parameters like current, pulse duration, and voltage on EDM performance. The findings indicate a crucial need for optimization to enhance productivity and product quality in manufacturing applications utilizing EDM.

1. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 6, Issue 2, February (2015), pp. 30-36© IAEME
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MODELING AND OPTIMIZATION OF EDM PROCESS
PARAMETERS: A REVIEW
S. G. Badekar1
, Dr. B. M. Dabade2
1
Department of Production Engineering, Government Polytechnic, Nanded, India,
2
Professor, Department of Production Engineering, SGGSIE&T, Nanded, India,
ABSTRACT
Electrical discharge machining (EDM) is one of the earliest non-traditional machining
processes. EDM process is based on thermo electric energy between the work piece and an electrode.
There are different input parameters which influence on the outputs such as MRR, TWR and SR.
Optimization is one of the techniques used in manufacturing sectors to arrive for the best
manufacturing conditions, which is an essential need for industries towards manufacturing of quality
products at lower cost. Also due to difficulty of EDM, it is very complicated to determine optimal
cutting parameters for improving cutting performance, which is an important action in machining.
And modeling is necessary to make a precise relation between input and output parameters. This
paper provide a review on the various research activities carried out in modeling and optimization of
EDM process parameters by various techniques on different dielectric media, material of work piece
and electrode material.
Keywords: Electrical Discharge Machining, Modeling, Optimization, Process Parameters
1. INTRODUCTION
Electric discharge machining (EDM) is an extensively used unconventional manufacturing
process that uses thermal energy of the spark to machine electrically conductive as well as non-
conductive parts regardless of the hardness of the work material. EDM can cut intricate contours or
cavities in pre-hardened steel or metal alloy (Titanium, Hastelloy, Inconel, etc.) without the need for
heat treatment to soften and re-harden the materials. During the EDM operation, tool does not make
direct contact with the work piece eliminating mechanical stresses, chatter and vibration problems.
EDM has thus, become an indispensable machining option in the meso and micro manufacturing of
difficult to machine complex shaped dies and molds and the critical components of automobile,
aerospace, medical and other industrial applications. The process has however, some limitations such
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2. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
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as high specific energy consumption, longer lead times and lower productivity which limit its
applications. Researchers worldwide are thus, focusing on process modeling and optimization of
EDM to improve the productivity and finishing capability of the process [6]. In this paper research
carried out by different researchers on various modeling and optimization techniques used in their
research work by using various electrodes, workpiece materials is presented.
Nomenclature:
EDM Electrical Discharge Machining
MRR Material Removal Rate
TWR Tool Wear Rate
SR Surface Roughness
RSM Response Surface Methodology
GRA Gray Regression Analysis
ANN Artificial Neural Network
FEM Finite Element Method
GA Genetic Algorithm
NN Neural Network
WEDM Wire Electrical Discharge Machining
T-off Pulse off
Ip Pulse Current
2. LITERATURE REVIEW
Literature reports wide experimental and analytical studies on process modeling and
optimization of EDM process to improve its accuracy and productivity.
Sushant Dhar et al. [1] evaluates the effect of current, pulse on time and air gap voltage on
MRR, TWR and radial over cut on EDM of Al-4 Cu-6 Si alloy-10 wt% SiCp composite by
developing a second order, non-linear mathematical model for establishing the relationship among
machining parameters. Analysis of Variance (ANOVA) has been performed to verify the fit and
adequacy of the developed mathematical models. They observed that current has major effect on
MRR followed by pulse duration and voltage. TWR also increases with increase in current. An
increase in current and pulse duration increases the radial over cut.
Ko-Ta Chig [2] proposed mathematical model for the modeling and analysis of the effects of
machining parameters on the performance characteristics in the EDM process of Al2O3+Tic mixed
ceramic which are developed using the response surface methodology to influence of machining
parameters on the performance characteristics of the material removal rate, electrode wear and
surface roughness. He found that the main two significant factors on the value of material removal
rate are discharge current and duty factor. The discharge current and the pulse on time also have
statistical significance on both the value of the electrode wear and surface roughness.
K. D. Chattopadhaya et al. [3] developed an empirical model for prediction of output
parameters using linear regression analysis by applying logarithmic data transformation of non-linear
equation. Three independent input parameters of the model viz. peak current, pulse on time and
rotational speed of tool electrode are chosen as variables for evaluating the output parameters such as
material removal rate, electrode wear ratio and surface roughness. Their result shows that peak
current and pulse on time are the most significant parameters for MRR and EWR. But peak current
and electrode rotation becomes the most significance parameter for SR. Their results further revealed
that maximizing the MRR while minimizing the EWR and improving the surface roughness, cannot
achieved simultaneously at a particular combination of control parameter setting.

3. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
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U. Esme et al. [4] uses two of the techniques, namely factorial design and neural network
(NN) were used for modeling and predicting the surface roughness of AISI 4340 steel. Surface
roughness was taken as a response variable measured after WEDM and pulse duration, open voltage,
wire speed and dielectric flushing pressure were taken as input parameters. Relationships between
surface roughness and WEDM cutting parameters have been investigated. They find that that, NN is
a good alternative to empirical modeling based on full factorial design.
M. K. Pradhan and C. K.Biswas [5] uses response surface methodology to investigate the
effect of four controllable input variables namely; discharge current, pulse duration, pulse off time
and applied voltage on surface roughness of electric discharge machined surface. They found that
discharge current, pulse duration and pulse off time and few of their interactions have significant
effect on the surface roughness.
Vasmi Krishna Pasam et al. [6] studied WEDM of titanium alloy for surface finish for the
various parameters using Taguchi parameter design. They develop mathematical model by means of
linear regression analysis to establish relationship between control parameters and surface finish as a
process response. They made an attempt to optimize the surface roughness prediction model using
Genetic Algorithm.
S.N.Joshi and S.S.Pande [7] develop physics based process modeling using finite element
method has been integrated with the soft computing techniques like a artificial neural networks and
genetic algorithm to improve prediction accuracy of the model with less dependency on the
experimental data. A two dimensional axi-symmetric numerical (FEM) model of single spark EDM
process has been developed based on more realistic assumptions such as Gaussian distribution of
heat flux, time and energy dependent spark radius, etc. to predict a comprehensive ANN based
process model to establish relationship between input process conditions and the process response.
They found the proposed integrated (FEM-ANN-GA) approach was efficient and robust as the
suggested optimum process parameters were found to give the expected optimum performance of the
EDM process.
Mohammadreza Shabgard et al. [8] carried out experimental studies to conduct a
comprehensive investigation on the influence of Electrical Discharge Machining (EDM) input
parameters on the characteristics of the EDM process. The studied process characteristics included
machining features, embracing material removal rate, tool wear ratio, and arithmetical mean
roughness, as well surface integrity characteristics comprised of the thickness of white layer and the
depth of heat affected zone of AISI H13 tool steel as workpiece. The experiments performed under
the designed full factorial procedure, and the considered EDM input parameters included pulse on-
time and pulse current. They find that The increase in pulse on-time leads to an increase in the
material removal rate, surface roughness, as well the white layer thickness and depth of heat affected
zone. The increase in pulse current leads to a sharp increase in the material removal rate and surface
roughness. The tool wear ratio decreases by the increase of pulse on-time, and increases by the
increase in the pulse current. A slight decrease could be observed in the white layer thickness by an
increase in the pulse current.
Md. Ashikur et al. [9] develops a single order mathematical model for correlation the various
electrical discharge machining parameters peak current, pulse on time and pulse off time and
performance characteristics surface roughness. They conduct experiments on Ti-6Al-4V with copper
electrode retaining the negative polarity as per the design of experiments. Response surface
methodology is utilized to develop the mathematical model as well as to optimize the EDM
parameters. They found that increasing the pulse on time causes fine surface until a certain value and
afterwards deteriorates in the surface finish.
Rametaz Ali Mahdavi Nejad [10] in their research optimize the surface roughness and
material removal rate of electro discharge machining of SiC parameters simultaneously. As the
output parameters are conflicting in nature, so there is no single combination of machining

4. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
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parameters, which provides the best machining performance. Artificial neural network (ANN) with
back propagation algorithm is used to model the process. A multi-objective optimization method,
non-dominating sorting genetic algorithm-II is used to optimize the process. Affects of three
important input parameters of process viz., discharge current, pulse on time (Ton), pulse off time
(Toff) on electric discharge machining of SiC are considered.
S. Gopalakannan et al. [11] investigate the influence of process parameters and their
interactions on MRR, EWR and SR of metal matrix composite of aluminum 7075 reinforced with 10
wt. % of B4C. They find that pulse current and pulse on time affect the MRR. The MRR first
increases with an increase in pulse on time and then decreases with further increase in pulse on time.
The pulse current and pulse on time have statistical significance on both EWR and SR. The higher
pulse off time offers lower the EWR value. The value of SR increases with increase in pulse current
and pulse on time.
Rajmohan T. et al. [12] investigate effect of electrical discharge machining parameters such
as pulse on time, pulse off time, voltage and current on MRR in 304 stainless steel by using Taguchi
method. They found that different combinations of EDM process parameters are required to achieve
higher MRR. The current and pulse off time are most significant machining parameter for MRR.
They also mention that based on minimum number of trails conducted to arrive at the optimum
cutting parameters. Taguchi method seems to be an efficient methodology to find the optimum
cutting parameters.
Pragya Shandilya et al [13] optimize the process parameters during machining of SiCp/6061
Al metal matrix composite by wire Electrical discharge machining using response surface
methodology and mathematical model have been developed to investigate the kerf, microstructure
and surface roughness. They found that input process parameters play a significant role in the
minimization of kerf.
R. Rajesh and M. Dev Anand [14] studied the effect of current, voltage, oil pressure, spark
gap, pulse on time and pulse off time on material removal rate and surface finish. Empirical models
for MRR and Ra have been developed by them by conducting a design of experiments based on the
Grey Relational Analysis. Genetic Algorithm based multi-objective optimization for maximization of
MRR and minimization of Ra has been done by using the developed empirical models.
Manish Vishwakarma et al. [15] illustrate in their research the influence of input machining
parameters on the material removal rate. They develop a mathematical model to formulate the input
current, gap voltage, and pulse on time and flushing pressure to the MRR of EN-19 alloy steel.
Analysis is carried out by them by using the response surface method and ANOVA analysis. They
find that the most significant EDM process variable influencing all the used machinability
parameters of EN-19 alloy steel is pulse current. The significance order of other parameters is gap
voltage followed by pulse on time and gap voltage.
Sanjay Kumar Majhi et al. [16] had done a hybrid optimization approach for the
determination of the optimal process parameters which maximize the material removal rate and
minimize surface roughness & the tool wear rate. The input parameters of electrical discharge
machining considered for this analysis are pulse current (Ip), pulse duration (T-on) & pulse off time
(T-off). The influences of these parameters have been optimized by multi response analysis. The
designed experimental results are used in the gray relational analysis & the weight of the quality
characteristics are determined by the entropy measurement method. The effects of the parameters on
the responses were evaluated by response surface methodology. Their result shows that the RSM,
GRA and entropy analysis can be successfully implemented to find the best parametric combination.
K. Kumar et al. [17] optimize the parameters of WEDM process by considering the effect of input
parameters viz. time on, time off, wire speed and wire feed for material removal and surface
roughness measurement by forming quadratic mathematical model and optimization is done through
Taguchi method.

5. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
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U. K. Vates and N.K.Singh [18] investigate the surface roughness process parameter
optimization during WEDM process for steel. Response Surface Methodology (RSM) is use to
investigate the effect of five independent input parameter namely gap voltage (Vg), Pulse on time
(Ton), pulse off time (T-off), wire feed (Wf) and flush rate (Fr) over CLA value of surface roughness
(Ra).A fractional factorial Design of Experiment of two level were employed to conducted the
experiment on EN-31 die steel with chromium coated copper alloy wire electrode. The responses
were observed by mathematical modeling using RSM on experimental data. They find that
Performance of WEDM largely depend not only upon the combination of material of workpiece and
wire electrode but also the optimal combination of the independent control process parameter.
S. Assarzadeh et al. [19] model and optimize process parameters in EDM of tungsten
carbide-cobalt composite using cylindrical copper tool electrodes in planning machining mode based
on statistical techniques. They select four independent input parameters viz. current, pulse on time,
duty cycle and gap voltage to assess the EDM process performance in terms of material removal rate,
tool wear and surface roughness. Response surface methodology has been used to plan and analyze
the experiments. They find that all the responses are affected by the rate and extent of discharge
energy but in a controversial manner.
Shashikant et al. [20] investigate the effect of various process parameters on the surface
roughness for EN19 material by using RSM. They observed that with these values improvement in
surface roughness were obtained.
Raghuraman S. and Narinder Kumar [21] investigate the optimal set of process parameters
such as current, pulse ON and OFF time in Electrical Discharge Machining (EDM) process to
identify the variations in three performance characteristics such as rate of material removal, wear rate
on tool, and surface roughness value on the work material for machining Mild Steel IS 2026 using
copper electrode. Based on the experiments conducted on L9 orthogonal array, analysis has been
carried out using Grey Relational Analysis, a Taguchi method. Response tables and graphs were
used to find the optimal levels of parameters in EDM process. The confirmation experiments were
carried out to validate the optimal results. Thus, the machining parameters for EDM were optimized
for achieving the combined objectives of higher rate of material removal, lower wear rate on tool,
and lower surface roughness value on the work material.
Shailendra Kumar Singh et al. [22] optimize the EDM parameters to get better surface finish on
Titanium alloys. Their result has given optimal combination of input parameters which give the
optimum surface finish on the EDM machined surface.
P. Balasubramanian and T. Senthilvelan [23] in their research work two different materials
EN-8 and D-3 steel have been used as a work pieces. The important process parameters that have
been selected are peak current, pulse on time, die electric pressure and tool diameter. The outputs
responses are MRR, TWR and SR. The cast copper and sintered powder metallurgy copper has been
used as tool electrode. They use response surface methodology to analyze the parameters and
ANNOVA has been applied to identify the significant process parameters. They find that for EN-8
material mean value of MRR is high and low TWR value for cast electrode compared with sintered
electrode, further more the SR value is marginally less for sintered electrode compared with cast
electrode and for D-3 steel which has been machined by cast electrode, the mean value of MRR is
high and TWR is low compared with sintered electrode, the mean value for SR is lower for sintered
electrode than that of cast electrode.
M.Azadi Moghaddam and F.Kolahan [24] in their study the effect of input EDM parameters
on the surface quality of AISI 2312 hot worked steel parts has been modeled and optimized. Their
experimental result for the optimal setting shows that there is considerable improvement in the
surface roughness; therefore, the proposed approach is quite capable in predicting and optimizing
EDM process output.

6. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
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Vikas et al. [25] compare the MRR for EN-19 and EN-41 material in die sinking EDM
machine. The various input factors like pulse on time, pulse off time, discharge current and voltage
were considered as the input processing parameters, while the MRR is considered as the output.
Optimization using Taguchi method was performed to predict the best combination of inputs towards
maximum output. They found that the discharge current in case of the En-41 and EN-19 material had
a larger impact as compared to other processing parameters on MRR.
3. CONCLUSION
The review is done based on previous and recent research on EDM. MRR, SR, TWR are the
performance measures of EDM. The performance of these process parameters are affected by various
process parameters during machining. The review paper evaluates various process modeling
techniques used during machining and various optimization techniques applied for improvement of
MRR, TWR and SR. This helps in identifying optimization parameters and modeling technique for
EDM work.
REFERENCES
1. Sushant Dhar, Rajesh Purohit, Nishant Saini, Akhil Sharma, G. Hemath Kumar, “Mathematical
modeling of electric discharge machining of cat Al-4Cu-6Si alloy-10 wt.% SiCp composite”,
Journal of Material Processing Technology 194 (2007) 24-29.
2. Ko-Ta Chiang, “Modelling and analysis of the effects of machining parameters on the
performance characteristics in the EDM process of Al2O3+TiC mixes ceramic” Int. J. Adv.
Manuf. Technol (2008) 37:pp.523-533.
3. K.D.Chattopadhya, S.Verma, P.S.Satsangi, P.C.Sharma, “Development of empirical model for
different process parameters during rotary electrical discharge machining of copper-steel (EN-8)
system”, J. Mater. Process. Technol. (2008).
4. U.Esme, A.Sagbas and F.Kahraman, “Prediction of surface roughness in wire electrical discharge
machining using design of experiments and neural networks”, Iranian Journal of Science &
Technology, Transaction B, Engineering, Vol. 33, No. B3, pp 231-240.
5. M. K.Pradhan and C. K.Biswas, “Modeling and analysis of process parameters on surface
roughness in EDM of AISI D2 tool steel by RSM approach”, International Journal of
Mathematical, Physical and Engineering Sciences, vol. 3:1, 2009, pp. 66-71.
6. Vamsi Krishna, Surendra Babu Battula, Swapna M., “Optimizing surface finish in WEDM using
the Taguchi parameter design method”, J. of the Braz. Soc. of Mech. Sci. & Eng. April-June
2010, Vol.XXXII, No. 2/ 107-113.
7. S.N.Joshi and S.S.Pande, “Intelligent process modeling and optimization of die-sinking electrical
discharge machining” in Elsevier Applied Soft Computing 11(2011) 2743-2755.
8. Mohammadreza Shabgard, Mirsadegh Seyedzavvar and Samad Nadimi Bavil Oliaei, “Influence
of Input Parameters on the Characteristics of the EDM process”, Journal of Mechanical
Engineering 57(2011)9, 689-696
9. Md. Ashikur Rahman Khan, M.M.Rahman, K.Kadigrama, M.A. Maleque, and M.Ishaq,
“Prediction of surface roughness of TI-6Al-4V in electrical discharge machining: A regression
model”, Journal of mechanical engineering and sciences volume I, December 2011, 16-24.
10. Ramezan Ali MahdaviNejad, “Modeling and optimization of Electrical Discharge Machining of
SiC parameters, using Neural Networks and Non-dominating Sorting Genetic Algorithm”,
Material science and applications, 2011, 2, 669-675.
11. S.Gopalakannan, T.Senthilven, S.Ranganathan, “Modeling and optimization of EDM process
parameters on machining of Al 7075-B4C MMC using RSM”, Elsevier Procedia Engineering 38
(2012) 685-690.

7. International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 6, Issue 2, February (2015), pp. 30-36© IAEME
36
12. Rajmohan T., Prabhu R., Subba Rao G., Palanikumar K., “Optimization of machining parameters
in Elecrical Discharge Machining(EDM) of 304 stainless steel”, Elsevier Procedia Engineering 38
(2012) 1030-1036.
13. Pragya Shandilya, P.K.Jain, N.K.Jain, “Parametric optimization during wire electric discharge
machining using response surface methodology”, Elsevier Procedia Engineering 38 (2012) 2371-
2377.
14. R.Rajesh and M.Dev Anand, “The optimization of the electro-discharge machining process using
response surface methodology and genetic algorithms”, Elsevier Procedia Engineering 38 (2012)
3941-3950.
15. Manish Vishwakarma, Vishal Parashar, V.K.Khare, “Regression analysis and optimization of
material removal rate on electrical discharge machine for EN-19 alloy steel” International Journal
of Scientific and Research Publications, Volume 2, Issu 11 Nov.2012.
16. Sanjay Kumar Majhi, M. K. Pradhan And Hargovind Soni, “Optimization of Edm Parameters
Using Integrated Approach of RSM, GRA and Entropy Method”, International Journal of Applied
Research In Mechanical Engineering (Ijarme) Issn: 2231 –5950, Volume-3, Issue-1, 2013, 82-87.
17. K.Kumar and R.Ravikumar, “Modeling and optimization of wire EDM process”, Int. Jou. Of
Modern Eng. Res. Vol.3, Issue 3, May-June 2013.1645-1648.
18. U.K.Vats and N.K.Singh, “Optimization of surface roughness process parameters of Electrical
discharge machining of EN-31 by response surface methodology”, International Journal of
Engineering Research and Technology. Volume 6, Number 6 (2013), 835-840.
19. S.Assarzadeh and M.Ghoreishi, “Statistical modeling and parametric optimization of process
parameters in electro-discharge machining of cobalt-bonded tungsten carbide composite
(WC/6%Co)”, Elsevier Procedia CIRP 6 (2013) 463-468.
20. Shashikant, Apurba Kumar Roy, Kaushik Kumar, “Effect and optimization of various machine
process parameters on the surface roughness in EDM for an EN 19 material using response
surface methodology”, Procedia Materials Science 5(2014) 1702-1709.
21. Raghuraman S, Thiruppathi K, Panneerselvam T and Santosh S, “Optimization of edm
parameters using taguchi method and grey relational analysis for mild steel is 2026”, International
Journal of Innovative Research in Science, Engineering and Technology, Vol. 2, Issue 7, July
2013, 3095-3104.
22. Shailendra Kumar Singh and Narinder Kumar, “Optimizing the EDM parameters to improve the
surface roughness of titanium alloy (Ti-6AL-4V)”, Int. Jour. Of Emerging Science and
Engineering, Vol.-1, Issue-10 Aug. 2013, 10-13.
23. P. Balasubramanian and T. Senthilvelan, “Optimization of machining parameters in EDM process
using cast and sintered copper electrode”, Elsevier, procedia materials science 6 (2014) 1292-
1302.
24. M. Azadi Moghaddam and F.Kolahan, “Modeling and Optimization of Surface Roughness of
AISI2312 Hot Worked Steel in EDM based on Mathematical Modeling and Genetic Algorithm”,
IJE TRANSACTIONS C: Aspects Vol. 27, No. 3, (March 2014) 417-424.
25. Vikas, Shashikant, A.K.Roy and Kaushik Kumar, “Effect and optimization of machine process
parameters on MRR for EN19 & EN41 materials using Taguchi”, Procedia Technology 14 (2014)
204-210.
26. Z. Ahmed and D. K. Mahanta, “A Comparative Study on Material Removal Rate by
Experimental Method and Finite Element Modelling In Electrical Discharge Machining”
International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 5,
2013, pp. 173 - 181, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.
27. Mane S.G. and Hargude N.V, “Parametric Optimization of Near Dry Electrical Discharge
Machining Process For Aisi Sae D-2 Tool Steel” International Journal of Mechanical Engineering
& Technology (IJMET), Volume 6, Issue 1, 2015, pp. 99 - 114, ISSN Print: 0976 – 6340, ISSN
Online: 0976 – 6359.