PARAMETRIC OPTIMIZATION OF NEAR DRY ELECTRICAL DISCHARGE MACHINING PROCESS FOR AISI SAE D-2 TOOL STEEL

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online), Volume 6, Issue 1, January (2015), pp. 99-114 © IAEME
99
PARAMETRIC OPTIMIZATION OF NEAR DRY
ELECTRICAL DISCHARGE MACHINING PROCESS FOR
AISI SAE D-2 TOOL STEEL
Mane S.G.1
, Hargude N.V.2
1,2
Department of Mechanical Engineering,
PVPIT Budhgaon, Sangli 416416,Maharashtra, India.
ABSTRACT
Present dissertation work has attempted to optimize the various significant process
parameters for near dry EDM process by Taguchi method and design of experiments. The response
variables are material removal rate (MRR), the surface roughness (SR) and tool wear rate (TWR). A
low cost mist delivery system (MQL fluid dispenser) to supply the mist (liquid-gas mixture) at a
controlled rate has been developed to conduct the experiments and has served it’s purpose
exceptionally well during the experimentation. The AISI SAE D-2 tool steel has been used as a
work-piece material. The kerosene air mixture has been used as a dielectric medium. The various
process parameters selected for the study were discharge current, gap voltage, pulse on time, duty
factor, air pressure and electrode material. A standard L18 orthogonal array was selected for design
of experiments. The results obtained from the experimental runs were analyzed by using Minitab15
software. ANOVA for S/N ratios was done to find the most contributing process parameters
affecting the MRR, TWR and SR. The best parametric settings for each of the maximum MRR,
minimum TWR and minimum SR were determined with the help of ANOVA. The corresponding
values of the response parameters were also calculated using mathematical formulae and confirmed
by performing validation experimentation. From the present experimental study, it is observed that
MRR, TWR and SR in near dry EDM process are mainly affected by the discharge current and
electrode material. Copper-tungsten electrode material exhibited lower SR and low TWR than that of
the copper electrode but higher MRR was obtained with copper electrode.
Keywords: Near dry EDM, Design of experiments, Taguchi method, ANOVA, MRR, SR, TWR.
INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN ENGINEERING
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ISSN 0976 - 6480 (Print)
ISSN 0976 - 6499 (Online)
Volume 6, Issue 1, January (2015), pp. 99-114
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1. INTRODUCTION
The metal working fluids (MWFs) are extensively used in conventional machining processes.
The economical, ecological and health impacts of metal working fluids (MWFs) can be reduced by
using minimum quantity lubrication referred to as near dry machining. In near dry machining
(NDM), an air-oil mixture called an aerosol is fed onto the machining zone [10]. This concept of
near dry machining can be well applied in EDM process, the process being referred to as near-dry
EDM process. Advantages of near-dry EDM were identified as a stable machining process at low
discharge energy input because the presence of liquid phase in the gas environment changes the
electric field, making discharge easier to initiate and thus creating a larger gap distance. In addition,
good machined surface integrity without debris reattachment that occurred in dry EDM was attained
since the liquid in the dielectric fluid enhances debris flushing. Other potential advantages of near-
dry EDM are a broad selection of gases and liquids and flexibility to adjust the concentration of the
liquid in gas. The dielectric properties can thus be tailored in near-dry EDM to meet various
machining needs, such as high MRR or fine surface finish. Also Near dry EDM shows advantages
over the dry EDM in higher material removal rate (MRR), sharp cutting edge, less debris deposition
and better surface finish. Compared to wet EDM, near dry EDM has higher material removal rate at
low discharge energy and generates a smaller gap distance [2]. A comparative study of wet, dry and
near dry EDM has been tabulated in Table 1. But the technical barrier in near-dry EDM lies in the
selection of proper dielectric medium and process parameters. From the review of literature it is seen
that experimental investigations have been carried out in order to study the effect of various input
parameters like discharge current, gap voltage, pulse on time, gas pressure, fluid flow rate, electrode
orientation and spindle speed on material removal rate, surface roughness and tool wear rate and to
improve the performance of near dry EDM process [1-6, 16].
Table1. Comparison of wet, dry and near dry EDM processes
Sr. No Aspect Wet EDM Dry EDM Near Dry EDM
1 Dielectric medium Liquids Gases Liquid-gas mixture
2 Dielectric used
Hydrocarbon based oils,
Kerosene, EDM oil,De-
ionized water
Air, Oxygen gas,
Argon gas, Nitrogen
gas, Helium gas etc
Water-air, Water-
oxygen, Kerosene-
air, Kerosene-
nitrogen mixtures etc.
3
Dielectric
consumption
Heavy ---- Very less
4 Pollution problem Major problem Odor of burning Very less
5 Fire Hazard
Highly flammable oils-
more fire hazard
No fire hazard No fire hazard
6 Energy input High Low Low
7 Process stability Good
Poor (arching
problem)
Good
8 Debris reattachment No Major problem No
9 Discharge Initiation ----- Difficult Easier
10 Dielectric properties Can’t be tailored Can be tailored Can be tailored
11 Gap Distance more ----- Less
12 Surface finish Good poor Good
13 Surface Integrity Good poor Good
14 MRR Lower Higher Higher

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976
6480(Print), ISSN 0976 – 6499(Online), Volume 6, Issue 1, January (2015), pp.
However, irrespective of its inherent advantages over wet and dry EDM processes, not much
attention has been given towards the parametric optimization of the near
necessary to optimize the input parameters for maximum material remova
the surface roughness (SR) to make the near dry EDM process cost effective and economically
viable one. In the present study, the best parametric settings for each of the maximum MRR,
minimum TWR and minimum SR have been determine
2. EXPERIMENTAL SET UP
The experimentation was carried out on the Electronica make smart ZNC sinker EDM. A
mist delivery system (MQL fluid dispenser) developed was used to supply the mist (kerosene
mixture) at a controlled rate to the gap between work
smaller quantity of liquid was formed and a very sharp and fine spray of the mist
became possible to machine the components using very small fluid flow
idea of minimum quantity lubrication (MQL)
of dielectric fluid (kerosene) and
near-dry in a true sense. The exper
developed for experimentation. The responses selected for
rate (MRR), tool wear rate (TWR) and surface roughness (SR). Response characteristics are given i
the Table 2.
Fig.1. Experimental Setup for Near
Response name
Material Removal Rate
(MRR)
Tool Wear Rate (TWR)
Surface Roughness (SR)
2.1. Selection of the process parameters and their levels
The process parameters and their levels given in
literature survey and considering the range limitation of EDM
levels for each of the parameters B, C, D, E and F are selected beca
Work piece
Electrode
Rotating tool Arrangement
anced Research in Engineering and Technology (IJARET), ISSN 0976
6499(Online), Volume 6, Issue 1, January (2015), pp. 99-114
101
attention has been given towards the parametric optimization of the near-dry EDM process. It is
necessary to optimize the input parameters for maximum material removal rate (MRR) and minimize
minimum TWR and minimum SR have been determined with the help of ANOVA
mist delivery system (MQL fluid dispenser) developed was used to supply the mist (kerosene
a controlled rate to the gap between work-piece & electrode. A perfect mist with a
formed and a very sharp and fine spray of the mist
became possible to machine the components using very small fluid flow rate of 4 ml/min. Hence the
idea of minimum quantity lubrication (MQL) could be implemented, consuming very small amount
of dielectric fluid (kerosene) and giving justice to the name of the process and making the process
The experimental setup shown in Figure 1 shows the mist delivery system
The responses selected for experimentation were material removal
rate (MRR), tool wear rate (TWR) and surface roughness (SR). Response characteristics are given i
Experimental Setup for Near- Dry EDM and sparking achieved
Table 2. Response Characteristics
Response type Unit
Material Removal Rate Larger the better, gm/min
Tool Wear Rate (TWR) Smaller the better gm/min
Surface Roughness (SR) Smaller the better Ra value in
microns
Selection of the process parameters and their levels
The process parameters and their levels given in Table 3 were selected based on extensive
literature survey and considering the range limitation of EDM machine [12, 14, 15, and 17]
levels for each of the parameters B, C, D, E and F are selected because the non
Spray gun
anced Research in Engineering and Technology (IJARET), ISSN 0976 –
14 © IAEME
dry EDM process. It is
l rate (MRR) and minimize
d with the help of ANOVA and S/N ratios.
mist delivery system (MQL fluid dispenser) developed was used to supply the mist (kerosene-air
. A perfect mist with a
formed and a very sharp and fine spray of the mist was achieved and it
rate of 4 ml/min. Hence the
, consuming very small amount
to the name of the process and making the process
shows the mist delivery system
experimentation were material removal
rate (MRR), tool wear rate (TWR) and surface roughness (SR). Response characteristics are given in
and sparking achieved
gm/min
gm/min
Ra value in
microns
able 3 were selected based on extensive
machine [12, 14, 15, and 17]. Three
use the non-linear behavior of
Sparking

102
process parameters can only be studied if more than two levels of a parameter are used [18]. Also
some of the constant parameters and their values or conditions selected for the experimentation are
tabulated in Table 4[12, 14, 15, and 17].
Table 3. Process parameters under study and their levels
Factors
Levels
Level 1 Level 2 Level 3
Electrode material (A) Copper-Tungsten Copper ------
Air pressure (B) kg/cm2
4 5 6
Discharge Current (C) Amps 8 12 16
Gap voltage (D) volts 40 60 80
Pulse on time (E) µs 100 150 200
Duty factor (F) % 7 9 11
Table 4. Constant parameters and their values / conditions for experimentation
2.2 Selection of the orthogonal array
In the present experiment, the L18 orthogonal array meets the requirements of experiment as it
is a smallest mixed 2-level and 3-level array [18]. The experimentation was carried out as per the L18
orthogonal array given in Table 5.
Table 5 Design of Experiments L18 (21
35
) array
Expt.
No.
Electrode
material
Air pressure
kg/cm2
Discharge
current Amps
Gap voltage
volts
Pulse on time Duty Factor
01 Cu W 4 8 40 100 7
02 Cu W 4 12 60 150 9
03 Cu W 4 16 80 200 11
04 Cu W 5 8 40 150 9
05 Cu W 5 12 60 200 11
06 Cu W 5 16 80 100 7
07 Cu W 6 8 60 100 11
08 Cu W 6 12 80 150 7
09 Cu W 6 16 40 200 9
10 Cu 4 8 80 200 9
11 Cu 4 12 40 100 11
12 Cu 4 16 60 150 7
13 Cu 5 8 60 200 7
14 Cu 5 12 80 100 9
15 Cu 5 16 40 150 11
16 Cu 6 8 80 150 11
17 Cu 6 12 40 200 7
18 Cu 6 16 60 100 9
Parameters Value/Condition
Work-piece material AISI SAE D2 tool steel
Work-piece size 50 mm * 50 mm * 6 mm
Tool electrode diameter 15 mm
Tool electrode rotating/stationary Rotating
Dielectric medium Kerosene-Air mixture
Fluid flow rate 4 ml/min
Polarity Straight (Electrode –ve, work-piece +ve)
Machining time 20 min.

103
2.3 Experimental procedure
Copper-Tungsten and copper as the tool electrode materials and kerosene-air mixture as the
dielectric medium were used for conducting the experiments [12]. A constant fluid flow rate of 4
ml/min for kerosene was maintained throughout the experimentation. The straight polarity (Electrode
–ve, work-piece +ve) was maintained during the experimentation [2]. A rotating tool arrangement
was used to keep the electrode rotating at a constant speed during machining [12]. The various
process parameters and their levels shown in table 5 were set while conducting each of the
experimental run. Total eighteen no. of experimental runs each of 20 min duration were carried out
as per the design matrix.
3. MEASUREMENT OF RESPONSE PARAMETERS
3.1 Measurement of MRR
MRR of each sample is calculated from weight difference of work piece before and after the
performance trial, which is given by:
‫ܴܴܯ‬ =
(ௐ௜ିௐ௙)
୲
݃݉/݉݅݊ (Equation ....1)
Where Wi = Initial weight of work piece material (gm)
Wf = Final weight of work piece material (gm)
t = Time period of trail in minutes
The weights of the work-pieces before and after machining for calculation of MRR were measured
using a weighing machine of Contech model CA-503.
3.2 Measurement of TWR
TWR of each sample is calculated from weight difference of tool electrode before and after
the performance trial, which is given by:
ܹܴܶ =
(்௜ି்௙)
௧
݃݉/݉݅݊ (Equation ....2)
Where Ti = Initial weight of tool electrode (gm)
Tf = Final weight of tool electrode (gm)
t = Time period of trail in minutes
The weights of the electrodes before and after machining for calculation of TWR were measured
using a weighing machine of Contech model CA-503.
3.3 Measurement of SR
Surface roughness was measured using the Surf Test model SJ210 of Mitutoyo, Japan.
Surface roughness of each sample was measured at three different locations of machined area and a
mean is taken.
3.4 Experimental Results and S/N ratios
The experimental results for material removal rate, tool wear rate and surface roughness by
varying the selected control parameters as per L18 orthogonal array are shown in Table 6. The S/N
ratios worked out by using MINITAB 15 software are also tabulated in Table 6.

104
Table 6. Results for MRR, TWR and SR
Expt.
No.
MRR gm/min TWR gm/min
SR
Ra
SN Ratio of
MRR
SN Ratio of
TWR
SN Ratio of
SR
01 0.00915 0.0004000 3.220 -40.7716 67.9588 -10.1571
02 0.01465 0.0005400 3.314 -36.6832 65.3521 -10.4071
03 0.06196 0.0006500 4.447 -24.1578 63.7417 -12.9613
04 0.00980 0.0004200 2.908 -40.1755 67.5350 -9.2719
05 0.01339 0.0006200 2.770 -37.4644 64.1522 -8.8496
06 0.09980 0.0007000 3.934 -20.0174 63.0980 -11.8967
07 0.00640 0.0003000 3.179 -43.8764 70.4576 -10.0458
08 0.08590 0.0008225 3.909 -21.3201 61.6973 -11.8413
09 0.07820 0.0006350 4.138 -22.1359 63.9445 -12.3358
10 0.05730 0.0008000 4.420 -24.8369 61.9382 -12.9084
11 0.01280 0.0007500 3.948 -37.8558 62.4988 -11.9275
12 0.07765 0.0011500 4.928 -22.1972 58.7860 -13.8534
13 0.08210 0.0008000 4.419 -21.7131 61.9382 -12.9065
14 0.06750 0.0018000 3.447 -23.4139 54.8945 -10.7488
15 0.09940 0.0013500 3.818 -20.0523 57.3933 -11.6367
16 0.06060 0.0011000 4.527 -24.3505 59.1721 -13.1162
17 0.08085 0.0015500 3.722 -21.8464 56.1934 -11.4155
18 0.07605 0.0005500 4.922 -22.3780 65.1927 -13.8428
4. RESULTS AND DISCUSSION
All observations are transformed into S/N ratio and results for S/N ratios of have been
analyzed by ANOVA method to find the significance of various control parameters and their best
level. The analysis and graphical presentations have been made using MINITAB 15 software. The
most significant parameters affecting the selected response variable and their best level value are
determined. The optimal design for each of the response parameter has been decided and confirmed
by conducting a confirmation test.
4.1 Analysis of Variance (ANOVA) for S/N ratios of MRR
The S/N ratio consolidates several repetitions into one value and is an indication of the
amount of variation present. The S/N ratios have been calculated to identify the major contributing
factors that cause variation in the MRR. MRR is “Larger is better” type response which is given by:
(S/N) LB = - 10 log (MSD) LB (Equation ….3)
Where (‫ ܤܮ)ܦܵܯ‬ =
ଵ
௡
∑ ൬
ଵ
௬೔
మ൰௡
௜ୀଵ (Equation …..4)
(MSD)LB = Mean Square Deviation for Larger-the-better response.
where, ‘y’ is value of response variable and ‘n’ is number of observations in the experiments.
Table 7 shows the ANOVA results for S/N ratio of MRR at 99 % confidence interval.
Discharge current was observed to be the most significant factor affecting the MRR, followed by
electrode material and gap voltage according to F test. All the remaining parameters namely, pulse
on time, duty factor and air pressure are insignificant to affect the MRR.

The percentage contribution of each of the control parameter can be calculated by the
following formula:
% contribution of control factor= [SS (Respective Factor)/SS (Total)] *100
For example, for discharge current,
% contribution = [375.61/1233.12]* 100 = 30.46 %
Table 7.Analysis of Variance for S/N ratios
Source DF Seq SS
Electrode
material
1 256.57
Air pressure 2 85.79
Discharge
current
2 375.61
Gap voltage 2 229.99
Pulse on Time 2 112.26
Duty Factor 2 132.97
Residual Error 6 39.93
Total 17 1233.12
S = 2.580 R
S: Significant factor; NS: Non
Fig. 2.Percentage contribution of control parameters for MRR
The percentage contribution of each of the control parameters under study for MRR is shown by
a pie chart in Figure 2. It can be seen that discharge current contributes
followed by electrode material (20.80 %) and gap voltage (18.66 %).
used to calculate mean of S/N ratios at three levels of all factors and are given in Table
rank of all factors in this study considering the mean of S/N ratios for MRR at different levels in
terms their relative significance. Current has the highest rank signifying highest contribution to
Gap voltage
Duty Factor
10.78
Pulse on Time
9.11
Air pressure
Percentage contribution of control parameters for MRR
105
(Equation ….5)
For example, for discharge current,
% contribution = [375.61/1233.12]* 100 = 30.46 %
Analysis of Variance for S/N ratios of MRR
Adj SS Adj MS F P
Contribution
256.57 256.572 38.5
5
0.00
185.79 42.593 6.45 0.03
2375.61 187.860 28.2
2
0.00
1229.99 114.994 17.2
8
0.00
3112.26 56.130 8.43 0.01
8132.97 66.487 9.99 0.01
239.93 6.695
100.00
S = 2.580 R-Sq = 96.8% R-Sq(adj) = 90.8%
S: Significant factor; NS: Non- significant factor
igure 2. It can be seen that discharge current contributes significantly (30.46 %),
followed by electrode material (20.80 %) and gap voltage (18.66 %). S/N ratio values of MRR are
used to calculate mean of S/N ratios at three levels of all factors and are given in Table
tudy considering the mean of S/N ratios for MRR at different levels in
Discharge current
30.46
Electrode material
20.8
Gap voltage
18.66
Air pressure
6.95
Error
3.24
Discharge current
Electrode material
Gap voltage
Duty Factor
Pulse on Time
Air pressure
Error
14 © IAEME
(Equation ….5)
Contribution
%
Remark
20.81 S
06.96 NS
30.46 S
18.65 S
09.10 NS
10.78 NS
03.24
100.00
Sq(adj) = 90.8%
significantly (30.46 %),
S/N ratio values of MRR are
used to calculate mean of S/N ratios at three levels of all factors and are given in Table 8. It gives us
tudy considering the mean of S/N ratios for MRR at different levels in
Discharge current
Electrode material
Gap voltage
Duty Factor
Pulse on Time
Air pressure
Error

106
MRR, followed gap voltage and electrode material. Air pressure has the lowest rank. Duty factor and
pulse on time were observed to be insignificant in affecting MRR.
Table 8.Response Table for Signal to Noise Ratios of MRR
Level
Electrode
material
Air Pr.
Discharge
current
Gap voltage
Pulse on
time
Duty factor
1 -31.84 -31.08 -32.62 -30.47 -31.39 -24.64
2 -24.29 -27.14 -29.76 -30.72 -27.46 -28.27
3 -25.98 -21.86 -23.02 -25.36 -31.29
Delta 7.55 5.10 10.80 7.70 6.03 6.65
Rank 3 6 1 2 5 4
cucuw
-21
-24
-27
-30
-33
654 16128
806040
-21
-24
-27
-30
-33
200150100 1197
Electrodemtl.
MeanofSNratios
air pr. dis. cu
gap vol pulseon time duty factor
MainEffects Plotfor SNratios
Data Means
Signal-to-noise: Larger is better
Fig. 3.Main effects plot for S/N ratios of MRR
Main effects plot for S/N ratios of MRR is shown in the Figure 3. The graph shows that with
increase in discharge current, S/N ratio increases. The S/N ratio increases with an increase in pulse
on time and air pressure as well.
As can be observed from the graph, S/N ratio decreases slightly with an increase in gap voltage from
40 V to 60 V. However, a steep increase in S/N ratio can be observed from a gap voltage of 60V to
80V. Further it can be observed that S/N ratio reduces with an increase in duty factor.
Lastly, it can be observed that out of the two electrode materials, Copper electrode has
larger S/N ratio compared to Copper-Tungsten electrode.
For optimizing a product or process design, S/N ratio is used because additivity of factor
effects is good when an appropriate S/N ratio is used. Otherwise, large interactions among the
control factors may occur resulting in high cost of experimentation and potentially unreliable results.
In optimization, we use S/N ratio as the objective function to be maximized[18]. To conclude the
discussion, for maximum MRR, the level value with higher S/N ratio of each of the control
parameter under study should be selected at this stage. Thus, with high discharge current of 16A,
high pulse on time of 200 µs, high gap voltage of 80 V, low duty factor of 7, higher air pressure of 6
kg/cm2
and copper electrode should be selected.
Thus, it can be concluded that the optimum combination for MRR is A2 B3 C3 D3 E3 F1.

107
After evaluating the optimal parameter settings, the next step of the Taguchi approach is to
predict and verify the enhancement of quality characteristics using the optimal parametric
combination, which is not available in L18 array under study. Hence theoretical optimum value of
MRR has to be calculated.
The estimated S/N ratio using the optimal level of the design parameters can be calculated. The
optimal value of S/N ratio is given by the formula
nopt=nm+∑a
i=1 (ni- nm) (Equation …..6)
where nm is the total mean S/N ratio, ni is the mean S/N ratio at optimum level and’ a’ is the number
of main design parameters that effect quality characteristic. Based on the above equation the
estimated multi-response signal to noise ratio can be obtained.
nopt = -28.0692+(-24.29+28.0692) + (-25.98+28.0692) + (-21.86+28.0692)
+ (-23.02+28.0692) + (-25.36+28.0692) + (-24.64+28.0692)
nopt = Optimal value of S/N ratio = -4.804
The corresponding value of MRR is given by the formula
‫ݕ‬ଶ
=
ଵ
ଵ଴
షƞ೚೛೟
భబ
(Equation …..7)
Thus, y2
= 0.3308
y opt = 0.5751 gm/min
A confirmation experiment is performed by setting the control parameters as per the optimum
levels achieved. The experimental result obtained for the MRR is 0.5628 gm /min. Thus, the
experimental value agrees reasonably well with prediction. The maximum deviation of predicted
result from experimental result is about 2.14 %. Hence, the experimental result confirms the
optimization of MRR using Taguchi method and the resulting model seems to be capable of
predicting MRR.
4.2 Analysis of Variance (ANOVA) for S/N ratios of TWR
The S/N ratios have been calculated to identify the major contributing factors that cause
variation in the TWR. TWR is “Smaller is better” type response which is given by:
(S/N) SB = - 10 log (MSD) SB (Equation …...8)
Where (‫ ܤܵ)ܦܵܯ‬ =
ଵ
௡
∑ (‫ݕ‬௜
ଶ
)௡
௜ୀଵ (Equation..…..9)
(MSD)SB = Mean Square Deviation for smaller-the-better response.
where, ‘y’ is value of response variable and ‘n’ is number of observations in the experiments.
Table 9 shows the ANOVA results for S/N ratio of TWR at 94 % confidence interval.
Electrode material was observed to be the most significant factor affecting the TWR, followed by
discharge current and gap voltage according to F test. All the remaining parameters namely, pulse on
time, duty factor and air pressure are insignificant to affect the TWR.
The percentage contribution of each of the control parameters under study for TWR is shown
by a pie chart in Figure 4. It can be seen that electrode material contributes significantly (47.7%),
followed by discharge current (17.68 %) and gap voltage (13.07%).
S/N ratio values of TWR are used to calculate mean of S/N ratios at three levels of all factors
and are given in Table 10. It gives us rank of all factors in this study considering the mean of S/N
ratios for TWR at different levels in terms their relative significance.

Table 9. Analysis of Variance for S/N ratios of TWR
Source DF Seq SS
Electrode 1 138.500
Air pr. 2 11.025
Discharge 2 51.344
Pulse on 2 19.621
Duty Factor 2 8.134
Total 17 290.363
S = 1.991
Fig. 4. Percentage contribution of control parameters for TWR
Table 10
Level
Electrode
material
1 65.33 63.38
2 59.78 61.50
3 62.78
Delta 5.55 1.88
Rank 1 5
Electrode material has the highest rank signifying highest contribution to TWR, followed by
discharge current and gap voltage. Duty factor has the lowest rank. Pulse on time and air pressure
were observed to be insignificant in affecting TWR.
Main effects plot for S/N ratios of TWR is shown in the
increase in discharge current from 8A to 12A, S/N ratio decreases. However as the discharge current
increases from 12A to 16A, S/N ratio go on increasing. The S/N ratio decreases with an increase in
pulse on time from 100 πs to 150
initially from 4 kg/cm2 to 5 kg/cm2 air pressure and shows an increasing trend as the air pressure
Discharge current
17.68
Gap voltage
13.07
Error
8.19
Pulse on Time
6.76
Percentage contribution of control parameters for TWR
108
Analysis of Variance for S/N ratios of TWR
Adj SS
Adj
MS
F P
Contribution
138.500 138.500 34.93 0.001 47.70
11.025 5.512 1.39 0.319 03.80
51.344 25.672 6.47 0.032 17.68
37.950 18.975 4.79 0.057 13.07
19.621 9.810 2.47 0.165 06.76
8.134 4.067 1.03 0.414 02.80
23.789 3.965 08.19
100.00
S = 1.991 R-Sq = 91.8% R-Sq(adj) = 76.8%
Table 10. Response Table for S/Noise Ratios of TWR
Air
Pr.
Discharge
current
Gap
voltage
Pulse on
time
63.38 64.83 62.59 64.02
61.50 60.80 64.31 61.66
62.78 62.03 60.76 61.98
1.88 4.04 3.56 2.36
5 2 3 4
material has the highest rank signifying highest contribution to TWR, followed by
were observed to be insignificant in affecting TWR.
ffects plot for S/N ratios of TWR is shown in the Figure 5. The graph shows that with
s to 150 πs. Further as the air pressure is increased S/N ratio decreases
Electrode material
47.7
Discharge current
Air
pressure
3.8
Duty
Factor
2.8
Electrode material
Discharge current
Gap voltage
Error
Pulse on Time
Air pressure
Duty Factor
14 © IAEME
Contribution
%
Remark
S
NS
S
S
NS
NS
100.00
Sq(adj) = 76.8%
Response Table for S/Noise Ratios of TWR
Pulse on
time
Duty
factor
61.61
63.14
62.90
1.53
6
material has the highest rank signifying highest contribution to TWR, followed by
igure 5. The graph shows that with
s. Further as the air pressure is increased S/N ratio decreases
Electrode material
Discharge current
Gap voltage
Pulse on Time
Air pressure
Duty Factor

109
increases to 6 kg/cm2. It can be seen that, as the gap voltage increases from 40 V to 60 V, S/N ratio
increases and shows a decreasing trend further as the gap voltage increases to 80 V. Also as the duty
factor increases from 7 to 9, S/N ratio increases with a slight decrease thereafter as the duty factor
increases to 11. It can be also observed that, Copper-Tungsten electrode has larger S/N ratio
compared to Copper electrode.
CuCuW
66.0
64.5
63.0
61.5
60.0
654 16128
806040
66.0
64.5
63.0
61.5
60.0
200150100 1197
Electrode matl
MeanofSNratios
Air Pr Disc. Currrent
Gap Voltage Pulse on Time Duty Factor
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
Fig. 5 Main effects plot for S/N Ratios of TWR
To conclude the discussion, for minimum TWR, the level value with higher S/N ratio of each
of the control parameter under study should be selected at this stage. Thus, a low discharge current of
8A, low pulse on time of 100 µs, moderate gap voltage of 60 V, moderate duty factor of 9, low air
pressure of 4 kg/cm2 and copper-tungsten electrode material should be selected. Thus, it can be
concluded that the optimum combination for TWR is A1 B1 C1 D2 E1 F2. This optimal parametric
combination is not available in L18 array under study. Hence theoretical optimum value of TWR has
to be calculated.
By using the equation 6 from section 4.1, the estimated multi-response signal to noise ratio
can be obtained.
nopt = 62.5525+(65.33-62.5525) + (63.38-62.5525) + (64.83-62.5525) + (64.31-62.5525)
+ (64.02-62.5525) + (63.14-62.5525)
nopt = Optimal value of S/N ratio = 72.2475
The corresponding value of TWR is given by the formula
‫ݕ‬ଶ
= 10
ష ƞ౥౦౪
భబ (Equation ……10)
Thus, y2
= 5.96005 * 10-08
y opt = 0.000244 gm/min
levels achieved. The experimental result obtained for the TWR is 0.000255gm /min. Thus, the
experimental value agrees reasonably well with prediction. The maximum deviation of predicted
result from experimental result is about 4.51 %. Hence, the experimental result confirms the
optimization of TWR using Taguchi method and the resulting model seems to be capable of
predicting TWR.

4.3 Analysis of Variance (ANOVA) for S/N ratios of SR
The S/N ratios have been calculated to identify the major contributing
variation in the SR. SR is “Smaller is better” type response which is given by
section 4.2. Table 11 shows the ANOVA results for S/N ratio of SR at 93 % confidence interval.
Electrode material was observed to be the
discharge current and air pressure according to F test. All the remaining
time, duty factor and gap voltage are non
Table 11 Analysis of Variance
Source DF Seq SS
Electrode
material
1 11.8250
Air pr. 2 5.6070
Discharge
current
2 11.3788
Pulse on Time 2 0.6359
Duty Factor 2 1.1095
Total 17 38.0423
S = 0.7862 R
Fig. 6 Percentage contribution of control parameters for SR
The percentage contribution of each of the control parameters under study for SR is shown by
a pie chart in Figure 6. It can be seen that electrode material contributes significantly (31.08 %),
followed by discharge current (29.91 %) and air pressure (14.74 %)
S/N ratio values of SR are used to calculate mean of S/N ratios at three levels of all factors
ratios for SR at different levels in terms the
rank signifying highest contribution to SR, followed by Electrode material and air pressure. Pulse on
time has the lowest rank. Gap voltage and duty factor were observed to be insignificant in aff
SR. One thing to be noted here is that
Air pr.
14.74
Gap voltage
9.93
Residual Error
9.75
Duty Factor
Percentage contribution of control parameters for SR
110
4.3 Analysis of Variance (ANOVA) for S/N ratios of SR
The S/N ratios have been calculated to identify the major contributing
variation in the SR. SR is “Smaller is better” type response which is given by
Table 11 shows the ANOVA results for S/N ratio of SR at 93 % confidence interval.
Electrode material was observed to be the most significant factor affecting the SR, followed by
discharge current and air pressure according to F test. All the remaining parameters i.e. pulse on
time, duty factor and gap voltage are non-significant to affect the SR.
Table 11 Analysis of Variance for S/N ratios of SR
Adj SS Adj MS F P
Contribution
%
11.8250 11.8250 19.13 0.005 31.08
5.6070 2.8035 4.54 0.063 14.74
11.3788 5.6894 9.20 0.015 29.91
3.7770 1.8885 3.05 0.122 9.93
0.6359 0.3179 0.51 0.622 1.67
1.1095 0.5548 0.90 0.456 2.92
3.7090 0.6182 9.75
100.00
S = 0.7862 R-Sq = 90.3% R-Sq(adj) = 72.4%
percentage contribution of each of the control parameters under study for SR is shown by
followed by discharge current (29.91 %) and air pressure (14.74 %).
are used to calculate mean of S/N ratios at three levels of all factors
R at different levels in terms their relative significance. Discharge current has the highest
time has the lowest rank. Gap voltage and duty factor were observed to be insignificant in aff
SR. One thing to be noted here is that there is very minute difference as far as the contribution of
Electrode material
31.08
Discharge current
29.91
Duty Factor
2.92
Pulse on Time
1.67
Percentage contribution of control parameters for SR
Electrode material
Discharge current
Air pr.
Gap voltage
Residual Error
Duty Factor
Pulse on Time
14 © IAEME
The S/N ratios have been calculated to identify the major contributing factors that cause
variation in the SR. SR is “Smaller is better” type response which is given by equations 8 and 9 in
Table 11 shows the ANOVA results for S/N ratio of SR at 93 % confidence interval.
most significant factor affecting the SR, followed by
parameters i.e. pulse on
Contribution
%
Remark
31.08 S
14.74 S
29.91 S
9.93 NS
1.67 NS
2.92 NS
9.75
100.00
Sq(adj) = 72.4%
percentage contribution of each of the control parameters under study for SR is shown by
are used to calculate mean of S/N ratios at three levels of all factors
Discharge current has the highest
time has the lowest rank. Gap voltage and duty factor were observed to be insignificant in affecting
there is very minute difference as far as the contribution of
Electrode material
Discharge current
Gap voltage
Residual Error
Duty Factor
Pulse on Time

111
electrode material and discharge current is concerned. But the analysis has ranked discharge current
as no1 and electrode material as no.2.
Main effects plot for S/N ratios of SR is shown in the Figure 7. The graph shows that with
increase in discharge current from 8A to 12A, S/N ratio increases. However as the discharge current
increases from 12A to 16A, S/N ratio go on decreasing. The S/N ratio decreases with an increase in
pulse on time. Further as the air pressure is increased S/N ratio increases initially from 4 kg/cm2
to 5
kg/cm2
air pressure and shows a decreasing trend as the air pressure increases to 6 kg/cm2
. It can be
seen that, as the gap voltage increases, S/N ratio shows a decreasing trend. Also as the duty factor
increases, S/N ratio increases. It can be also observed that, Copper-Tungsten electrode has larger S/N
ratio compared to Copper electrode.
Table 12. Response Table for Signal to Noise Ratios of SR
Level
Electrode
material
Air Pr.
Discharge
current
Gap voltage Pulse on time Duty factor
1 -10.86 -12.04 -11.40 -11.12 -11.44 -12.01
2 -12.48 -10.89 -10.86 -11.65 -11.69 -11.59
3 -12.10 -12.75 -12.25 -11.90 -11.42
Delta 1.62 1.21 1.89 1.12 0.46 0.59
Rank 2 3 1 4 6 5
CuCuW
-11.0
-11.5
-12.0
-12.5
-13.0
654 16128
806040
-11.0
-11.5
-12.0
-12.5
-13.0
200150100 1197
Electrode mtl.
MeanofSNratios
Air pr. Dis. current
gap voltage Pulse on time Duty factor
MainEffects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
Fig. 7 Main effects plot for S/N ratios of SR
To conclude the discussion, for minimum SR, the level value with higher S/N ratio of each of
the control parameter under study should be selected at this stage. Thus, a moderate discharge
current of 12A, low pulse on time of 100 µs, low gap voltage of 40 V, higher duty factor of 11,
moderate air pressure of 5 kg/cm2
and copper-tungsten electrode material should be selected.
Thus, it can be concluded that the optimum combination for SR is A1 B2 C2 D1 E1 F3.
This optimal parametric combination is not available in L18 array under study. Hence theoretical
optimum value of SR has to be calculated.
By using the equation 6 from section 4.1, the estimated multi-response signal to noise ratio can
be obtained.

112
nopt = -11.6735+(-10.86+11.6735) + (-10.89+11.6735) + (-10.86+11.6735)
+ (-11.12+11.6735) + (-11.44+11.6735) + (-11.42+11.6735)
nopt = Optimal value of S/N ratio = -8.2225
The corresponding value of SR is given by the equation 10 (see sect.4.2).
Thus, y2
= 6.64125
y opt = 2.577 µm
levels achieved. The experimental result obtained for the SR is 2.668 µm. Thus, the experimental
value agrees reasonably well with prediction. The maximum deviation of predicted result from
experimental result is about 3.53 %. Hence, the experimental result confirms the optimization of SR
using Taguchi method and the resulting model seems to be capable of predicting SR.
5. CONCLUSIONS
1. The MRR, TWR and SR in near dry EDM process are mainly affected by the discharge current
and electrode material.
2. Copper-tungsten electrode material exhibited lower SR and low TWR than that of the copper
electrode but higher MRR was obtained with copper electrode.
3. Increase in the discharge current leads to an increase in the MRR but deteriorating the surface
finish (higher SR values).However, an increase in discharge current initially increases the
TWR but at higher discharge currents TWR was found to be decreasing.
4. The process parameters pulse on time and duty factor were found to be insignificant to affect
the selected responses under study viz. MRR, TWR, and. SR. Air pressure was found to be
significant to affect only SR.
5. Higher material removal rate (MRR) can be achieved with high discharge current and high gap
voltage with copper electrode.
6. Low tool wear rate (TWR) can be achieved with lower discharge current and moderate gap
voltage with copper-tungsten electrode.
7. Low surface roughness (SR) values (Better surface finish) can be achieved with moderate
discharge current and moderate air pressure with copper-tungsten electrode.
6. FUTURE SCOPE
1. The AISI SAE D-2 tool steel has been used as a work-piece material and copper and copper-
tungsten are used as tool electrode materials in the present work. Copper infiltrated graphite
is also a good candidate for tool electrode material. Various combinations of electrode
materials and liquid gas mixtures as dielectric mediums can be tried to check the feasibility of
near dry EDM process for various work piece materials.
2. Various concentrations of liquid in gas can be tried. The combination of additional liquids
such as hydrocarbon oil, and gases such as nitrogen, oxygen, and helium can be tried in near
dry EDM with a goal to tailor unique properties of the EDM dielectric fluid to achieve
machining efficiency and quality improvements, such as high MRR and fine surface
roughness in near dry EDM.
3. The machined surface and subsurface properties, such as microstructure, micro hardness,
residual stress, and material composition, can be investigated to characterize the near-dry
EDM process.
4. The topographical analysis of the machined surfaces by near dry using SEM images can be
done to study the presence of micro-cracks, blowholes and dimples and the surface integrity.

113
5. Multi objective optimization can be done by using techniques like gray relational analysis by
using the same experimental results of the present work.
6. Apart from experimental work, ample scope exists for theoretical modeling and process
simulation in near dry EDM. Current literature is insufficient in this regard.
7. Practical application of the near dry EDM process can bring a lot of advantages for machine
makers and machine end users. Important factor is the simplicity of the machine construction,
not requiring sophisticated and specious dielectric circulation and cooling system. The
design, manufacturing and material costs can be reduced.
7. REFERENCES
1. C.C. Kao,Jia Tao, Albert J. Shih,”Near Dry Electrical Discharge Machining”, International
Journal of Machine Tools & Manufacture 47 (2007), 2273-2281.
2. Jia Tao, Albert J. Shih,Jun Ni,”Experimental study of the Dry & Near-Dry Electrical
Discharge Milling Processes”, Journal of Manufacturing Science & Engineering (Feb2008),
Vol.130 / 011002-1- 011002-8.
3. Y Jia, B.S. Kim, D.J. Hu & J Ni, “ Parametric study on near-dry wire electrodischarge
machining of polycrystalline diamond-coated tungsten carbide material”, Proceedings of the
Institution of Mechanical Engineers, Part B : Journal of Engineering Manufacture (2010) Vol.
224, 185-193.
4. M. Fujiki, Gap-Yong Kim, Jun Ni, Albert J. Shih,”Gap control for near-dry EDM milling
with lead angle”, International Journal of Machine Tools & Manufacture 51 (2011), 77-83.
5. M. Fujiki, Jun Ni, Albert J. Shih,” Investigation of the effect of electrode orientation & fluid
flow rate in near-dry EDM milling”, International Journal of Machine Tools & Manufacture
49 (2009), 749-758.
6. Jia Tao, Albert J. Shih, Jun Ni,” Near-Dry EDM Milling of Mirror-Like Surface Finish”,
International Journal of Electrical Machining 13 ( January 2008). 29-33.
7. P. Govindan, Suhas S. Joshi, “ Experimental characterization of material removal in dry
electrical discharge drilling” , International Journal of Machine Tools & Manufacture 50
(2010), 431-443.
8. Fabio N. Leao , Ian R. Pashby , “ A review on the use of environmentally-frendly dielectric
fluids in electrical discharge machining”, Journal of Materials Processing Technology 149
(2004), 341-346.
9. Sourabh K. Saha, “ Experimental Investigation of the Dry Electrical Machining process”, A
thesis submitted in partial fulfillment of the requirements for the degree of Master of
Technology to the Department of Mechanical Engineering, Indian Institute of Technology
Kanpur, (Apr. 2008)
10. Viktor P. Astakhov, General Motors Business Unit of PSMI, USA, “ Ecological Machining :
Near Dry Machining”.
11. Jeffy Joseph, Department of Mechanical Engineering, College of Engineering,
Thiruvananthapuram, The University of Kerala,(Nov.2009).
12. Jia Tao,“Investigation of Dry & Near Dry Electrical Discharge Milling Process”, A
dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of
Philosophy (Mechanical Engineering) in The University of Michigan, (2008).
13. Sourabh K. Saha, S.K.Choudhury, Department of Mechanical Engineering, Indian Institute
of Technology Kanpur, ”Multy-objective optimization of the dry electric discharge
machining process”, (Jan. 2009).

114
14. S.H.Tomadi, M.A.Hassan, Z. Hamedon, “Analysis of the Influence of EDM Parameters on
Surface Quality, Material Removal Rate and Electrode Wear of Tungsten Carbide”,
Proceedings of the International Multi Conference of Engineers and Computer Scientists Vol.
II (2009).
15. Singh S., Maheshwari S., Pandey P.C, “Some investigations into the electric discharge
machining of hardened tool steel using different electrode materials”, Journal of Materials
Processing Technology, (2004), Vol.149, pp. 272–277. 126
16. P. R. Cheke, D. S. Khedekar R. S. Pawar, M. S. Kadam, “Comparative performance of wet
and near dry EDM process for machining of oil hardened non sinking steel material”,
International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –
6340 (Print), Volume 3, Issue 2, (May-August 2012),pp.13-22
17. Shankar Singh, S. Maheshwari, P.C. Pandey, “Some investigations into the electric discharge
machining of hardened tool steel using different electrode materials”, Journal of Materials
Processing Technology 149 (2004), pp. 272–277.
18. M. S. Phadke, “Quality Engineering Using Robust Design”, AT and T Laboratories, Prentice
Hall, Englewood Cliffs, New Jersey 07632.
19. Mane S.G., Hargude N.V., PVPIT Budhgaon, Sangli, “An Overview of Experimental
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22 - 36, ISSN Print: 0976-6480, ISSN Online: 0976-6499.
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PARAMETRIC OPTIMIZATION OF NEAR DRY ELECTRICAL DISCHARGE MACHINING PROCESS FOR AISI SAE D-2 TOOL STEEL

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PARAMETRIC OPTIMIZATION OF NEAR DRY ELECTRICAL DISCHARGE MACHINING PROCESS FOR AISI SAE D-2 TOOL STEEL