30120140507011

International INTERNATIONAL Journal of Mechanical JOURNAL Engineering OF and MECHANICAL Technology (IJMET), ISSN ENGINEERING
0976 – 6340(Print),
ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 113-124 © IAEME
AND TECHNOLOGY (IJMET)
ISSN 0976 – 6340 (Print)
ISSN 0976 – 6359 (Online)
Volume 5, Issue 7, July (2014), pp. 113-124
© IAEME: www.iaeme.com/IJMET.asp
Journal Impact Factor (2014): 7.5377 (Calculated by GISI)
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113

IJMET
© I A E M E
OPTIMIZATION OF ELECTRODE WEAR RATE ON ELECTRICAL
DISCHARGE MACHINING AISI 304 SS WITH MULTI HOLE ELECTRODE
Dr. Maan Aabid Tawfiq, Saad Hameed Najem
Department of Production Eng. and Metallurgy, University of Technology, Baghdad, Iraq
ABSTRACT
AISI 304 stainless steel have a wide range of applications in the industrial field. The need for
machining AISI 304 SS has not been eliminated fully. The electrode wear rate (EWR) is an
important aspect during electrical discharge machining (EDM). In this investigation an attempt has
been made to assess the factors influencing electrode wear rate on the machining of AISI 304 SS.
Design of experiments (full factorial design) concept has been used for experimentation. The
machining experiments were conducted on a die sinking EDM machine using two levels of factors.
The factors considered were electrode shape, pulse current, pulse on time and pulse off time. A
procedure has been developed to assess and optimize the chosen factors to attain minimum electrode
wear rate by incorporating: (i) response table and response graph; (ii) normal probability plot; (iii)
interaction graphs; (iv) analysis of variance (ANOVA) technique. The results indicated that
electrode shape is a factor, which has greater influence on EWR, followed by pulse off time. Also the
determined optimal conditions really reduce the EWR on the machining of AISI 304 SS within the
ranges of parameters studied.
Keywords: EDM; AISI 304 SS; EWR; Response Table; Response Graph; ANOVA; Normal
Probability Plot.
1. INTRODUCTION
In today’s manufacturing scenario, electrical discharge machining (EDM) contributes a prime
share in the manufacture of complex- shaped dies, molds, and critical parts used in automobile,
aerospace, and surgical components with high precision [1]. The material is removed from the work
piece by the thermal erosion process, i.e., by a series of recurring electrical discharges between a
cutting tool acting as an electrode and a conductive workpiece in the presence of a dielectric fluid
[2]. The performance of EDM is usually evaluated by the output parameters namely material
removal rate (MRR), electrode wear rate (EWR), wear ratio (WR), machined surface roughness, etc.

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),
[3-5]. It is always desirable to obtain higher MRR with lower EWR, WR, and Ra .With the electrical
sparks, more material is removed from the work material because of its positive polarity [6].
However, Due to high temperature of the sparks not only work material is melted and vaporised but
the electrode material is also melted and vaporised which is expressed by EWR [7].Electrode Wear is
an important factor because it affects dimensional accuracy and the shape produced [8]. The selected
AISI 304 stainless steel material for the present investigation is having a wide range of applications
in the industrial field: Chemical, Pharmaceutical, Cryogenic, Food, Dairy, Paper industries etc. [9].
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This experimental scheme is almost similar to the L16 orthogonal array. A procedure has
been introduced to optimize the chosen factors to attain minimum tool wear by incorporating (i)
response table and effect graph; (ii) normal probability plot; (iii) interaction graphs; (iv) analysis of
variance (ANOVA) technique.
2. ACHIEVE THE DESIRED EWR ON THE AISI 304 SS WORKPIECE
In order to achieve the desired EWR on the AISI 304 SS workpiece, the present investigation
has been planned in the following steps:
A: identifying the important factors
Based on the previous published results [10, 11], the machining parameters, which are having
significant effect on EWR. The machining parameters identified are: (1) electrode shape; (A), (2)
pulse current; (B), (3) pulse on time; (C), (4) pulse off time; (D). Out of which electrode shape has
been specially applied.
B: finding the upper and lower limits of the factors identified
For finding the upper and lower limits of the machining parameters, a detailed analysis has
been carried out. The limits identified are discussed below:
i. The studies related to EDM indicate that electrode shape is the factor, which highly influences the
EWR, the increase of number of holes inside the electrode (to certain limit) decreases the EWR [12].
For maintaining the proper EWR, the electrode shape has been set at reasonable level of being multi
4 -channels and multi 7 -channels.
ii. Pulse current is another important factor which influences EWR. With an increase of pulse
current, EWR increases [10]. In the present study, the Pulse current is 30 and 36 Amp.
iii. The EWR increases with increase in pulse duration at all value of peak current. [13].The pulse on
time chosen between 100 and 150 μsec.
iv. The EWR decreases when pulse-off time is increased [14] and hence the pulse-off time has been
selected at reasonable level and is 50 and 75 μsec.
C: developing the experimental design matrix using design of experiments
An experiment is a series of trials or tests, which produces quantifiable outcomes. The
experiment may be random or deterministic. For experimentation, design of experiment in statistics
has been used. The merit of this experimental scheme is that the cost of experimentation is reduced
considerably as compared to one factor at a time type experiment. The identified factors and its
lower and upper limits are discussed in sections 2.A and 2.B. In this experimental scheme, all
possible combinations of levels are included so that there are 2n (where n refers to the number of

factors, i.e., 24 = 16) trials in the experiment [15]. The notations, units and their levels chosen are
summarized in Table 1.
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For easy recording and processing of experimental data, the parameters levels are coded as
+1 and −1. The intermediate coded value of any levels can be calculated by using the following
expression [16]:

=
…………………………... (1)
Where Xmax is the upper level of the parameter, Xmin is the lower level of the parameter and
Xiis the required coded values of the parameter of any value of X from Xmin to Xmax.
D: conducting the experiments as per the design matrix
The Electrical Discharge Machining experiments were conducted at the Center of Training
and Workshop at University of Technology, Iraq. The experiments were performed on a die sinking
EDM machine (CHMER EDM CNC) which operates with an iso-pulse.
The workpiece material used in this study was AISI 304 SS. Prior to EDM processing, the
workpiece was square specimens 35 × 35 mm and 3 mm thick with a roughness Ra of 2 μm on the
surface to be machined. The chemical composition of the workpiece material is given in Table 2.
Electrolytic copper cylindrical bars 100 mm long with a diameter of 30 mm were mounted axially in
line with workpieces and used as tool electrode at positive polarity, through hole was drilled as
shown in Fig. 1.
The gap between the electrode and the workpiece is 0.20 mm. The chemical composition of
the electrode material is given in Table 3.
Commercial grade EDM oil was used as dielectric fluid. Pressure flushing with 0.3 Kg/cm2
was used. A digital balance (DENVER INSTRUMENT) with a resolution of 0.1 mg was used for
weighing the electrode before and after the machining process. The EWR (mm3/min.) is defined as
the mass of metal removed M2 from the original massM1 (g) divided by the density of electrode
(g/mm3), and T is the machining time (min). Eq. (2) show the calculations used for assessing the
values of EWR. The design matrix and the corresponding responses are given in Table 4.

……………………………….….. (2)
Table 1: Important parameters and their levels
S.
no
Parameter Notation Unit
Levels
Actual factors
Coded
factors
Low high Low Low
1
Electrode
shape
A -
Multi -
4channels
Multi – 7
channels
-1 +1
2 Pulse current B Amp 30 36 -1 +1
3 Pulse on time C μsec. 100 150 -1 +1
4 Pulse off time D μsec. 50 75 -1 +1
Table 2: Chemical composition of 304 stainless steel workpiece
Material C% Si% Mn% Cr% Ni% Mo% Cu% Fe%
% weight 0.07 0.64 1.4 18.5 10.2 0.3 0.1 Balance

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Table 3: Chemical composition of tool electrode
Material Fe% Si% Sb% Zn% Sn% Al% P% Cu%
% weight 0.015 0.01 0.002 0.004 0.004 0.004 0.002 Balance
Fig. 1: Geometry of the tool electrode and the workpiece
Table 4: Design matrix and corresponding output response
Exp.
No
Coded factors Actual factors EWR
A B C D A B C D mm3/min.
1 -1 -1 -1 -1 Multi 4 channels 30 100 50 1.115
2 +1 -1 -1 -1 Multi 7 channels 30 100 50 0.885
3 -1 +1 -1 -1 Multi 4 channels 36 100 50 1.152
4 +1 +1 -1 -1 Multi 7 channels 36 100 50 0.923
5 -1 -1 +1 -1 Multi 4 channels 30 150 50 1.232
6 +1 -1 +1 -1 Multi 7 channels 30 150 50 0.985
7 -1 +1 +1 -1 Multi 4 channels 36 150 50 1.267
8 +1 +1 +1 -1 Multi 7 channels 36 150 50 1.012
9 -1 -1 -1 +1 Multi 4 channels 30 100 75 0.970
10 +1 -1 -1 +1 Multi 7 channels 30 100 75 0.785
11 -1 +1 -1 +1 Multi 4 channels 36 100 75 1.005
12 +1 +1 -1 +1 Multi 7 channels 36 100 75 0.813
13 -1 -1 +1 +1 Multi 4 channels 30 150 75 1.082
14 +1 -1 +1 +1 Multi 7 channels 30 150 75 0.865
15 -1 +1 +1 +1 Multi 4 channels 36 150 75 1.102
16 +1 +1 +1 +1 Multi 7 channels 36 150 75 1.012
3. INFLUENCES OF MACHINING PARAMETERS AND THEIR EFFECTS ON EWR
The effect of factors which influences the EWR on AISI 304 SS has been analyzed through:
A. Response table and effect graph
The influence of machining parameters on EWR has been performed using response table.
Response tables are used to simplify the calculations needed to analyze the experimental data. In
response table, the effect of a factor on a response variable is the change in the response when the
factor goes from it slow level to its high level. The complete response table for a two level, 16 run
full factorial experimental design is shown in Table 5. If the effect of a factor is greater than zero,
the average response is higher for the higher level of the factor than for the low level. However, if
the estimated effect is less than zero, it indicates that the average response is higher at low level of

the factor than at high level. If the effect for a factor is very small, then it is probably because of
random variation than areal factor effect. The graphical display such as response graph can be used,
in conjunction with a response table, to identify appropriate settings for machining parameters to
minimize the average EWR [16]. The effect of main and interaction factors derived from the
response table for machining process is plotted in Fig. 2. The procedure involved in the construction
of a response table and response graph is explained elsewhere [17]. From Fig. 2, it is inferred that
the larger the vertical line, the larger the change in EWR when going from level -1 to level +1 for a
factor. It will be pointed out that the statistical significance of a factor is directly related to the
length of the vertical line.
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Fig. 2: Effect graph
B. Normal probability plot
In response graph, it is found that some of the factor effects are larger than the other, but it is
not clear, whether these results are real or chance. To identify the real effects, normal probability plot
are used and is shown in Fig. 3. Normal plot is a graphical technique based on ‘‘Central limit
theorem’’. The procedure for constructing the normal probability plot is given elsewhere [18]. As per
the normal probability plot, points which are close to a line fitted to the middle group of points
represent estimated factors which do not demonstrate any significant effect on the response variable.
On the other hand, the points appear to be far away from the straight line are likely to represent
thereal factor effects on the EWR. From Fig. 3, it has been asserted that the main factors A, B, C
and D and their five factor interactions AB, AD, BC, BD and CD are away from the straight line and
are considered to be significant.
C. ANOVA technique
The normal probability plot has the disadvantage of not providing a clear criterion for what
values for estimated effects indicate significant factor or interaction effects. In addition, how do we
measure amount of departure from the straight line pattern .ANOVA meets this need by how much
an estimate must differ from zero in order to be judged “statistically significant”. The ANOVA result
is presented in Table 6. This analysis has been carried out for a level of significance of 5%, i.e., for a
level of confidence of 95%. From the ANOVA results, it is concluded that the factors A, B, C, D and
their interactions AB, AD, BC, BD and CD have significant effect on EWR and AC has no effect at
95% confidence level. As the interaction effect of AB, AD, BC, BD and CD seems to be significant
to the EWR, the average values of the EWR are calculated for all the combinations. By using the
values of interaction, the significant interaction graphs are drawn for each combination of levels. The
significant interactions between the parameters (AB, AD, BC, BD and CD) are shown in Figs. 4 and
8. The insignificant interactions (AC) is presented in shown in Fig 9.

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Table 5: Response table for electrode wear rate (EWR)
S. No
EWR
mm3/min.
A B C D AB AC AD
-1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1
1 1.115 1.115
1.115
1.115
1.115
1.115
1.115
1.115
2 0.885 0.885 0.885
0.885
0.885
0.885 0.885 0.885
3 1.152 1.152
1.152 1.152
1.152
1.152 1.152 1.152
4 0.923 0.923 0.923 0.923
0.923
0.923 0.923
0.923
5 1.232 1.232 1.232
1.232 1.232
1.232 1.232
1.232
6 0.985 0.985 0.985 0.985 0.985
0.985
0.985 0.985
7 1.267 1.267 1.267 1.267 1.267
1.267 1.267 1.267
8 1.012 1.012 1.012
1.012 1.012
1.012
1.012 1.012
9 0.970 0.970 0.970 0.970
0.970 0.970
0.970 0.970
10 0.785 0.785 0.785 0.785
0.785 0.785 0.785 0.785
11 1.005 1.005
1.005 1.005
1.005 1.005
1.005 1.005
12 0.813 0.813 0.813 0.813
0.813 0.813 0.813 0.813
13 1.082 1.082 1.082
1.082
1.082 1.082 1.082 1.082
14 0.865 0.865 0.865
0.865
0.865 0.865 0.865 0.865
15 1.102 1.102
1.102
1.102
1.102 1.102
1.102 1.102
16 1.012
1.012
1.012
1.012
1.012
1.012
1.012
1.012
Average 1.012 1.115 0.910 0.989 1.035 0.956 1.069 1.071 0.954 1.005 1.019 1.011 1.014 0.995 1.030
Effect
-0.205
0.046 0.113 -0.117 0.014 0.003 0.035
S. No
BC BD CD ABC ABD ACD BCD ABCD
-1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1 -1 +1
1
1.115 1.115 1.115 1.115 1.115 1.115 1.115 1.115
2
0.885 0.885 0.885 0.885 0.885 0.885 0.885 0.885
3 1.152 1.152 1.152 1.152 1.152 1.152
1.152 1.152
4 0.923 0.923 0.923 0.923 0.923 0.923 0.923 0.923
5 1.232
1.232 1.232
1.232 1.232
1.232 1.232 1.232
6 0.985
0.985 0.985
0.985
0.985 0.985
0.985 0.985
7
1.267 1.267
1.267 1.267 1.267
1.267 1.267
1.267
8
1.012 1.012
1.012
1.012 1.012
1.012 1.012 1.012
9
0.970 0.970
0.970 0.970 0.970 0.970
0.970 0.970
10 0.785 0.785 0.785 0.785 0.785 0.785 0.785 0.785
11 1.005
1.005 1.005
1.005 1.005
1.005 1.005
1.005
12 0.813 0.813 0.813 0.813 0.813 0.813 0.813 0.813
13 1.082 1.082 1.082 1.082 1.082 1.082 1.082 1.082
14 1.102 1.102 1.102 1.102 1.102
1.102 1.102
1.102
15
1.102 1.102 1.102 1.102 1.102 1.102
1.102 1.102
16
1.012 1.012 1.012 1.012 1.012 1.012 1.012 1.012
Average
1.036 1.018 1.036 1.018 1.008 1.046 1.034 1.020 1.034 1.021 1.005 1.050 1.035 1.020 1.033 1.021
Effect
-0.018 -0.018 0.038 -0.014 -0.003 0.045 -0.015 -0.012
In this figures the lines are parallel to each other, which show that there is no interaction
between parameters. By analyzing these figures also evident from ANOVA analysis, it has been
concluded that CD and AD are more interactive than other interactions.

S. no Factors Estimated effects Effect squared
(E2)
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Table 6: ANOVA test results
DOF Mean square
Fig. 3: Normal probability plot
(MS)
Fratio
1 A -0.205 0.042 1 0.042 84
2 B 0.046 0.002 1 0.002 4
3 C 0.113 0.012 1 0.012 24
4 D -0.117 0.013 1 0.013 26
5 AB 0.014 0.0002 1 0.0002 0.4
6 AC 0.003 0.000009 1 0.000009 0.018
7 AD 0.035 0.0012 1 0.0012 2.4
8 BC -0.018 0.0003 1 0.0003 0.6
9 BD -0.018 0.0003 1 0.0003 0.6
10 CD 0.038 0.0014 1 0.0014 2.8
11 ABC -0.014 0.0002
12 ABD -0.003 0.000009
13 ACD 0.045 0.002
14 BCD -0.015 0.0002
15 ABCD -0.012 0.0001
Error 5 0.0005

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Fig. 4: Interaction between A and B Fig. 5: Interaction between A and D
Fig. 6: Interaction between B and C Fig. 7: Interaction between B and D
Fig. 8: Interaction between C and D Fig. 9: Interaction between A and C
4. OPTIMIZING THE CHOSEN FACTOR LEVELS TO ATTAIN MINIMUM EWR
From the analysis of response graph, response table, and interaction graphs, the optimal
machining parameters for the AISI 304 SS machining process is achieved for the minimum value of
EWR. The optimal conditions arrived are:
(A) Electrode shape at high level (7 channels).
(B) Pulse current at low level (30 Amp).

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(C) Pulse on time at low level (100 μsec.).
(D) Pulse off time at high level (75 μsec.).

Based on the above optimum conditions, the minimum value of EWR can be obtained from
the following expression using the values form response table (Table 5) [16, 18].
EWR (min.)= [grand mean] + [contribution of A] + [contribution of B] + [contribution of C] +
[contribution of D] + [contribution of AB] + [contribution of AD] + [contribution of BC] +
[contribution of BD] + [contribution of CD]
………………………………………………………..(3)
EWR (min.)= + [A (+1)− ] + [B (−1)− ] + [C (−1)− ] + [D (+1)− ] +[AB (−1)− ] + [AD (+1)− )] +
[BC (+1)− )] + [BD (−1)− )] + [CD (−1)− )]
EWR (min.)=1.012 + [0.910 − 1.012] + [0.989 –1.012] + [0.956 – 1.012] + [0.954 – 1.012] + [1.006
−1.012] + [1.030 – 1.012] + [1.018 – 1.012] + [1.036 – 1.012] + [1.008 – 1.012]
EWR (min.)= 0.807 0.80 mm3 /min.
The above result reveal that the minimum electrode wear on the machining of AISI 304 SS
within the range of factor under investigation is 0.80 mm3/min. To check the validity of the
optimization procedure, the aforementioned EWR value is compared with the experimental values,
obtained for the same optimized conditions and the variation is within the reasonable accuracy
(+5%). Moreover, the equation mentioned earlier (3) can be effectively used to predict the EWR of
EDMed AISI 304 SS drills at a 95% confidence level by multiplying the contributions with
corresponding coded values of the main and interaction factors.
5. DISCUSSIONS
The electrode wear depends on the dielectric flow in the machining zone. If the flow is too
turbulent, it results in an increase in electrode wear. Pulsed injection of the dielectric has enable
reduction of wear due to dielectric flow. [19]
From the results, it can be seen that the EWR is maximum at Multi - 4 channels electrode
shape. The EWR decreases with the increase of number of holes inside the electrode. The reason
being at multi 7 - channels, debris removed from efficient machining area more than multi 4 -
channels thus multi 7 - channels brings fresh dielectric in the inter electrode gap.
It has been observed that EWR increases with increase of peak current (from 30 to 36 Amp). Higher
current density available at the working gap, at higher peak current conditions, generates large
amount of heat. This rapidly overheats the electrode and increases electrode wear rate as it is
concluded in Ref. [20].
It was observed that EWR increases with increment in pulse-on-time. This is because of
formation of ‘black layer’ at tool surface. The black layer formation is due to migration of workpiece
element and carbon from dielectric to the tool electrode surface. This finding has close relationship
with the results presented by Ref. [21].
Also the increase in pulse-off-time decreased the EWR as with long pulse-off time the
dielectric fluid produces the cooling effect on electrode and work piece and hence decreases the
EWR [14].
The observed results shown proved that the main factor, which affects the electrode wear
rate, is electrode shape. The pulse-on-time only plays small role on EDM process. The results

indicated that the electrode wear rate is minimum at a pulse-on-time of 100 μsec. In machining, the
interaction between the chosen factors also plays some role in deciding the electrode wear rate. The
results indicate that the interactions namely AB, AD, BC, BD and CD have significant effect on
electrode wear rate. Out of four main factors considered, electrode shape is the most significant
factor which affecting the electrode wear rate; while pulse-on-time is the least significant parameter.
Among the interactions, the interaction pulse-on-time and pulse-off-time is more significant than
other parameters.
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6. CONCLUSION

Using design of experiments technique, the parameters, which are having significant
influence on electrode wear rate on the machining of AISI 304 SS, have been studied.
(1) This experimental technique is easier and convenient technique used to study the main and
interaction effects of different influential combinations of machining parameters affecting
electrode wear rate.
(2) Electrode shape is the factor, which has greater influence on EWR, followed by pulse off time.
(3) The interaction also play some role in deciding the EWR on the EDM of AISI 304 SS. The
interaction between pulse on time and pulse off time has more influence comparing with other
interactions on EWR on the machining of AISI 304 SS.
(4) The parameters considered in the experiments are optimized to attain minimum EWR using
response graph, response table, normal probability plot, interaction graphs and analysis of
variance (ANOVA) technique.
(5) The optimization procedure can be used to predict the EWR for EDM of AISI 304 SS within
the ranges of variable studied. However, the validity of the procedure is limited to the range of
factors considered for the experimentation.
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