Optimization of EDM Process Parameters using Response Surface Methodology for AISI D3 Steel

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume: 3 | Issue: 3 | Mar-Apr 2019 Available Online: www.ijtsrd.com e-ISSN: 2456 - 6470
@ IJTSRD | Unique Paper ID – IJTSRD23535 | Volume – 3 | Issue – 3 | Mar-Apr 2019 Page: 1651
Optimization of EDM Process Parameters using
Response Surface Methodology for AISI D3 Steel
Dr. N. Mahesh Kumar1, Mr. P. Chinna Rao2
1Associate Professor, 2
Assistant Professor
1Department of Mechanical Engineering, Srivenkateswara college of Engineering and Technology,
1Srikakulam, Andhra Pradesh, India
2Department of Mechanical Engineering, RGUKT-AP, IIIT, Srikakulam, Andhra Pradesh, India
How to cite this paper: Dr. N. Mahesh
Kumar | Mr. P. Chinna Rao
"Optimization of EDM Process
Parameters using Response Surface
Methodology for AISI D3 Steel"
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ABSTRACT
The present work demonstrates the optimization process of material removal
rate (MRR) of electrical discharge machining (EDM) by RSM (Response Surface
Methodology). The work piece material was EN31 tool steel. The pulse on time,
pulse off time, pulse current and voltage were the control parameters of EDM.
RSM method was used to design the experiment using rotatable central
composite design as this is the most widely used experimental design for
modeling a second–order response surface. The process has been successfully
modeled using response surface methodology (RSM) and model adequacy
checking is also carried out using Minitab software. The second-order response
models have been validated with analysis of variance. Finally, an attempt has
been made to estimate the optimum machining conditions to produce the best
possible responses within the experimental constraints.
KEYWORDS: CNC Machining, EDM, Material Removal Rate (MRR), Response
Surface Methodology
I. INTRODUCTION
Technologically advanced industries like aeronautics,
automobiles, nuclear reactors, missiles, turbines etc.,
requires materials like high strength temperature resistant
alloys which have higher strength, corrosion resistance,
toughness, and other diverse properties. With rapid
development in the field of materials it has becomeessential
to develop cutting tool materials and processes which can
safely and conveniently machine such new materials for
sustained productivity, high accuracy and versatility at
automation. Consequently, non-traditional techniques of
machining are providing effective solutions to the problem
imposed by the increasing demand for high strength
temperature resistant alloys, the requirement of parts with
intricate and compacted shapes and materials so hard as to
defy machining by conventional methods. Electrical
Discharge Machining is a non-traditional machining
technique, which is widely used to produce finish parts
through the action of an electrical discharge of short
duration and high current density between the tool and
work piece. The tool and the work piece are free from the
physical contact with each other. Generally, the EDM is used
for machining of electrical conductive materials in the
presence of a dielectric fluid. These are submersed in a
dielectric liquid such as kerosene or deionized water. Its
unique feature of using thermal energy to machine
electrically conductive partsregardlessofhardness has been
its distinctive advantage. The electrical dischargemachining
process is widely used in the aerospace, automobile, die
manufacturing and plastic mould industriestomachinehard
metals and its alloy [1]. The basic principle in EDM is the
conversion of electrical energy into thermal energy through
a series of discrete electrical discharges occurring between
the electrode and work piece immersed in the dielectric
fluid. The insulating effect of the dielectric is important in
avoiding electrolysis of the electrodes during the EDM
process. A spark is produced at the point of smallest inter-
electrode gap by a high voltage, overcoming the strength
dielectric breakdown strength of the small gap between the
cathode and anode at a temperature in the range of 8000 to
12,000 °C. Erosion of metal from both electrodes takesplace
there. The numerical control monitors the gap conditions
(voltage and current) and synchronously controls the
different axes and the pulse generator. The dielectric liquid
is filtrated to remove debris particles and decomposition
IJTSRD23535

International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470
@ IJTSRD | Unique Paper ID - IJTSRD23535 | Volume – 3 | Issue – 3 | Mar-Apr 2019 Page: 1652
products [2].The prediction of optimalmachiningconditions
for good surface finish plays a very importantrolein process
planning. Speedingand Wang[3] haveattempted tooptimize
the process parametric combinations by modeling the
process using ANN and characterize the surface in wire
electrical discharge machining (WEDM) on AISI420through
time series techniques. Zhang et al. [4] have investigatedthe
effects on material removal rate, surface roughness and
diameter of discharge points in electro-dischargemachining
(EDM) on ceramics. From the experimental results, they
have shown that the material removal rate, surface
roughness and the diameter of discharge point all increase
with increasing pulse-on time and discharge current. Tsai
and Wang [5] have established a semi-empirical model of
surface finish on work for various materials (three different
grades of steel) in electrical discharge machining and the
parameters of the model viz. peak current, pulse duration,
electric polarity and properties of materials have been fitted
based on the experimental data using Taguchi method. It is
seen that the developed model is dependent on work and
tool materials. Singh et al. [6] have developed a model for
multi-response optimization of process parameters viz.
metal removal rate, tool wear rate, taper, radial overcut and
surface roughness on electrical discharge machining of Al-
10%SiCp composites. Yih-fong and Fu-chen [7] have
presented an approach for optimizing high-speed electrical
discharge machining (EDM) using Taguchi methods. They
have concluded that the most important factors affectingthe
EDM process robustness have been identified as pulse-on
time, duty cycle, and pulse peak current. Fig. 1 shows
Fishbone diagram showing parameters affecting MRR.
Figure 1 Fishbone diagram showing parameters affecting MRR
II. EXPERIMENTATION
The design of experiments technique is a very powerful tool, which permits to carry out the modeling and analysis of the
influence of process variables on the response variables. Improving the MRR and surface quality are still challenging problem
that restrict the expanded application of the technology. Semi-empirical models of MRR for various work piece and tool
electrode combinations have been presented by various researchers. The influence of pulse current, pulse time, duty cycle,
open circuit voltage and dielectric flushing pressure over the MRR and surface roughness on EN 31 tool steel have also been
studied. The optimum processing parameters are very much essential to boost up the production rate to a large extent and
shrink the machining time, since these materials, which are processed by EDM are costlyandtheprocessisvery expensivetoo.
The rotatable central composite design is the most widely used experimental design for modeling a second–order response
surface. A design is called rotatable when the variance of the predicted response at any point depends only on the distance of
the point from the center point of design. Table I shows the components of central composite second order rotatable design.
Table 1 Components of central composite second order Rotatable Design
Variable(K) Fractional Point(2k) Start point 2K Center Point Total (N) Value of α
3 8 6 6 20 1.682
4 16 8 7 31 2
5 16 10 6 32 2
6 32 12 9 53 2.378
As the number of variables is 4, a total of 31 experiments were planned for this investigation. Experiments were carried out
using CNC EDM (EMT 43) Electronica die sinking machine. Table II shows the specification of die sinking EDM machine.
Table 2 Specifications of die sinking EDM machine
Machining conditions
Machine Used CNC EDM (EMT 43) (Electronica)
Electrode Electrolytic Copper ( 99.9% Purity)
Electrode polarity Positive
Workpiece Oil Hardened Non Shrinking Steel ( 48 – 50 RC)
Dielectric EDM Oil
The composition of AISI D3 steel work-piece material used for experimentation in this work is given in Table 1.

Table 3 Chemical composition of AISI D3 steel (wt %)
Material C Cr Mn Mo V Si Ni
AISI D3 2.05 11.10 0.589 0.042 0.055 0.498 0.065
An electrolytic pure copper with 25 mm X 25 mm is used as a tool electrode (positive polarity).The machiningparameters and
their levels are shown in Table 4.
Table 4 Different variables used in the experiment and their levels
Variable coding
Level
1 2 3
Pulse On (Ton) in μs A 200 300 400
Pulse Off (Toff) in ìs B 1800 1700 1600
Discharge Current (Ip) in A C 8 12 16
Voltage (V) in V D 40 60 80
The parameter MRR is selected as response variable, which refers to the machining efficiency of the EDM process and defined
as follows: MRR (gm/min) = : Where, Wi = Initial weight of work piece material (gms), Wf= Final weight of work piece
material (gms),t = Time period of trials in minutes The work piece is weighed before and after each experiment using an
electric balance to determine the value of MRR. For efficient evolution of the EDM process, the larger MRR is regarded as the
best machining performance.
III. METHODOLOGY
In statistics, Response surfacemethodology(RSM)investigatestheinteraction between severalillustrativevariablesandoneor
more response variables. Box and Draper [9] were introducing RSM in 1951.The most important purpose of RSM is to use a
series of designed experiments to attain an optimal response. A second-degree polynomial model is use in RSM.Thesemodels
are only an approximation, but used because such a model is easy to estimate and apply, even when little is known about the
process. The process of RSM includes designing of a series of experiments for sufficient and reliable measurement of the
response and developing a mathematical model of the second order response surface with the best fittings. Obtaining the
optimal set of experimental parameters, thus produce a maximum or minimum value of the response. The Minitab Software
was used to analyze the data [10]
IV. RESULT AND DISCUSSION
Using the experimental results for MRR (Table IV), response surface model is developed and analysis of variance(ANOVA)for
the adequacy of the model is then performed in the subsequent step. The F ratio is calculated for 95% level of confidence.
Table 5 ANOVA table for MRR (before elimination) Estimated Regression Coefficients for MRR
Term Coefficient SE Coefficient T p
Constant 0.22752 0.00508 44.787 0
A 0.000681 0.002744 0.248 0.807
B 0.053207 0.002744 19.393 0
C 0.096158 0.002744 35.049 0
D -0.0431 0.002744 -15.71 0
A*A -0.00201 0.002513 -0.799 0.436
B*B -0.00335 0.002513 -1.331 0.202
C*C 0.012858 0.002513 5.116 0
D*D 0.01777 0.002513 7.07 0
A*B -0.00214 0.00336 -0.636 0.534
A*C 0.00251 0.00336 0.747 0.466
A*D 0.000303 0.00336 0.09 0.929
B*C 0.021314 0.00336 6.343 0
B*D -0.013 0.00336 -3.868 0.001
C*D -0.01826 0.00336 -5.433 0
S = 0.0134406 PRESS = 0.0166486
R-Sq = 99.21% R-Sq(pred) = 95.46% R-Sq(adj) = 98.52%
After eliminating the non-significant terms, the final response equation for MRR is given as follows:
MRR = 0.222579 + ( 0.000681 X Ton ) + (0.053207 X Toff )+( 0.096158 X Ip )- (0.043101 X V ) +
(0.013372 X Ton
2 ) +(0.018285 X V2 )+ (0.021314 X Toff X Ip )- (0.012999 X Toff )- (0.018288 X Ip X V)
The final model is tested for variance analysis (F-test) and indicates that the adequacy of the test is established which are
justified with F-values. For the analysis the data, the checking of goodness of fit of the model is very much required.Themodel
adequacy checking includes the test for significance of the regressionmodel, testforsignificanceon modelcoefficients, andtest
for lack of fit. For this purpose, analysis of variance (ANOVA) is performed. Table V shows the ANOVA for the response, MRR.
The fit summary recommends that the quadratic model is statistically significant for analysis of MRR.

Table 6 Analysis of Variance for MRR
Source DF Seq. SS Adj. SS Adj. MS F P
Regression 9 0.363588 0.363588 0.040399 244.87 0
Linear 4 0.334449 0.334449 0.083612 506.8 0
Square 2 0.013834 0.013834 0.006917 41.93 0
Interaction 3 0.015304 0.015304 0.005101 30.92 0
Residual Error 21 0.003465 0.003465 0.000165
Lack-of-Fit 15 0.003465 0.003465 0.000231
Pure Error 6 0 0 0
Total 30 0.367052
The normal probability plot of residuals for MRR is illustrated in Fig 2.It is expected that data from experiments form anormal
distribution. It reveals that the residual fall on a straight line, implying that the errors arespread inanormaldistribution.Here
a residual means difference in the observed value (obtained from the experiment) and the predicted valueorfitted value.This
is also, confirmed by the variations between the experimental results and model predicted values analyzed through residual
graphs, and are presented in Fig 3.
Figure 2 Normal Probability Figure 3 Residual Plot
Figure4. Main Effects Plot
From this main effects plot , Figure 4 it is clear that the parameters pulse off and current have highest inclination, so these are
most significant but pulse on and voltage are nearly horizontal, so these are non- significant.
The parametric analysis has been carried out to study the influences of the input process parameter such asTon,Toff,IpandV
on the process response, MRR during die-sinkingEDMprocess. Contourplotsandthree-dimensionalresponsesurfaceplots are
formed based on the quadratic model to evaluate the variationofresponse.The plotsareshown in Figures5-10.Theseplotscan
also give further assessment of the correlation between the process parameters and response as under:
1. MRR increases with increase in Ip and Ton. This is due to higher spark energy from high temperature. (Figure 2. MRR
decreases with increase in Toff.
2. Increase in applied voltage also increase MRR.
Figure5. Variation of MRR according to change of Ip and
Ton Hold value: Toff =1700(µs), V= 60
Figure6. Variation of MRR according to change of Hold
value: Ton=300(µs), V =60

Fig7.Variation of MRR according to change of voltage and
current and Hold on Values: Ton=300(μs), Toff= 1700(μs)
Fig8. Variation of MRR change of voltage Hold value: Ip=
12A, Toff=1700(μs)
Fig9. Variation of MRR according to change of Toff Ip=
12A Ton=300(ìs) voltage
Fig 10.Variation of MRR according to change of Hold value:
Toff and Hold value: V=60, Ip= 12A
To check the developed model one confirmation test is carried out at the mid-levels of the process parameters. Table 5and 6
shows the result of the confirmation run for MRR. It is observed that the calculated error is small(about2%)Thisconfirmsthe
reproducibility of experimental conclusion.
Table 7Conformation test result and comparison with predicted result as per model
Ton (μs) Toff (ìs) Ip (A) V (Volt)
MRR(gm/min)
Experimental Model Predicted error (%)
300 1700 12 60 0.22752 0.222579 2.17
Finally an optimum condition is obtained from RSM with an objective of maximumMRRwithintheexperimentalrangeandthe
levels of the process parameters are Pulse on 500 μs, Pulse off 1500 μs, Current 20 A and Voltage 60 V.
V. CONCLUSION
Experimentalinvestigationonelectricaldischargemachining
of EN 31 tool steel is performed with a view to correlate the
process parameterswiththe responsesforMRR.Theprocess
has been successfully modeled using response surface
methodology (RSM) and model adequacy checking is also
carried out. The second-order response models have been
validated with analysis of variance. Finally, an attempt has
been made to estimate the optimum machiningconditionsto
produce the best possible response within the experimental
constraints. This study can help researchers and industries
for developing a robust, reliable knowledge base and early
prediction of MRR without experimentingwithEDM process
for EN 31 tool steel.
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