International Research Journal of Engineering Science, Technology and Innovation (IRJESTI) Vol. 1(5) pp.142-151, August 2012
Available online http://www.interesjournals.org/IRJESTI
Copyright © 2012 International Research Journals
Full Length Research Paper
Determination of an optimum parametric combination
using a surface roughness in EDM process through
response surface methodology
R. Rajesh* and M. Dev Anand
Department of Mechanical Engineering, Noorul Islam Centre for Higher Education,Kumaracoil – 629180, Kanyakumari
District, Tamilnadu, India.
Accepted 26 June, 2012
Electrical Discharge Machining (EDM) process is an important manufacturing technology in machining
difficult-to-cut and shape complicated contours. It provides an economical and effective method for
shaping and smoothing high strength, heat resistant materials into intricate shapes. The EDM process
involves a controlled erosion of electrically conductive materials by the initiation of rapid and repetitive
spark discharge between the electrode tool and work piece, separated by a small gap of about 0.05 0.5mm known as the spark gap. This spark gap is immersed under a dielectric fluid. In EDM process, it
is important to select machining parameters for achieving optimal machining performance in high heat
resistance and hard material. The desired machining parameters are found out from the hand book
based on experience. However, this does not ensure that the obtained parameters are near optimal
performance. To meet the challenges of a changing world, the EDM process must have the process
capability, accuracy and robustness. To achieve this goal the EDM process has to be modeled and
optimized. In this research, Response Surface Methodology is used to investigate the effect of five
controllable input variables namely peak current, discharge voltage, pulse on time, pulse off time, oil
pressure and SR is the response. To study the proposed second-order polynomial model for SR, a
Central Composite Design (CCD) is used to estimate the model coefficients of the five input parameters.
Experiments were conducted on AISI 1020 steel with copper electrode. The response is modeled using
RSM on experimental data. The significant coefficients are obtained by performing Analysis of Variance
(ANOVA) at 5% level of significance. It is found that peak current, discharge voltage, pulse on time,
pulse off time and oil pressure and along with their interactions have significant effect on the SR.
Keywords: Electrical discharge machining, response surface methodology, central composite Design, analysis
of variance.
INTRODUCTION
Electric Discharge Machining (EDM), is now a well known
process particularly used in precise machining for
complex shaped work pieces, as an alternative to more
traditional approaches and for details concerning the
Physical phenomena inherent to this process, one can
consult Nizar Ben Salah, et al., 2006. This process
enabels machining of any material, which is electrical
conductive, irrespective of its hardness, shape and strength.Even highly delicate sections and weak materials can
be machined without any fear of distortion because there
*Corresponding Author Email: anandpmt@hotmail.com and
rajesh200345@yahoo.co.in
is no direct contact between the tool and work piece.
Since the invention of EDM in the 1940s,many efforts
have been made to improve the machining performance
and stability of EDM process due to continuous process
improvement. This necessity leads to evolution of
advance materials like high strength alloys, ceramics,
fiber-reinforced composites etc. In machining of these
materials, conventional manufacturing processes are
increasingly being replaced by more advanced
techniques, which use different fashion of energy to
remove the material because these advance materials
are difficult to machine by the conventional machining
processes, and it is difficult to attain good surface finish
and close tolerance.
Rajesh and Anand 143
With the advancement of automation technology
manufacturers are more fascinated in the processing and
miniaturization of components made by these costly and
hard materials. EDM has grown over the last few
decades from a novelty to a mainstream manufacturing
process. It is most widely and successfully applied for the
machining of various work piece materials in the said
advance industry. It is a thermal process with a complex
metal removal mechanism, involving the formation of a
plasma channel between the tool and work piece
electrodes, the repetitive spark instigate melting and even
evaporating the electrodes. In the recent years, EDM is
firmly established for the production of tool to produce
die-castings, plastics a moulding, forging dies etc. The
advantage of EDM process is its capability to machine
difficult to machine materials with desired shape and size
with a required dimensional accuracy and productivity.
Due to this benefit EDM is an illustrious technique used in
modern manufacturing industry to produce high-precision
machining of all types of conductive materials, alloys and
even ceramic materials, of any hardness and shape,
which would have been difficult to manufacture by
conventional machining. However, the efficiency of
machining is low as compared to conventional machining.
Though EDM process is very demanding but the
mechanism of process is complex and far from
completely understood. Therefore, it is troublesome to
establish a model that can accurately predict the
performance by correlating the process parameter. The
optimum processing parameters are very much essential
to establish to boost up the production rate to a large
extent and shrink the machining time, since these
materials, which are processed by EDM and even the
costly process is very costly.
Quite a lot of research attempts have been made for
modeling of EDM process and investigation of the
process performance to recuperate MRR. Improving the
MRR and surface quality are still challenging problems
that restrict the expanded application of the technology.
Little research has been reported about EDM on AISI
1020 steel yet for the modeling by, surface response
methodology. In this paper, surface response approach is
used for development of a model and analysis of SR, with
peak current (Ip), pulse on time (Ton), pulse off time (Toff),
discharge voltage and oil pressure as input parameters.
A Central Composite Design (CCD) for combination of
variables and Response Surface Method (RSM) have
been used to analyze the effect of the five parameters,
current (Ip), pulse on time (Ton), pulse off time (Toff),
discharge voltage and oil pressure on the SR of EDM
process. Su et al., 2004 developed an ANN model of the
EDM process and further used it to optimize the input
process parameters by using GA. Sen and Shan 2007,
Mandal et al., 2007, Gao et al., 2008, Snoyes, 1971, and
Wang et al., 2003 followed the similar methodology for
thermo deleing and optimization of EDM process for
different work–tool material pairs. Recently, Yanga et al.,
2009 used Simulated Annealing (SA) technique with ANN
for optimization of MRR and surface roughness.
Literature Survey
Different researchers did the various investigations about
EDM. The results were summarizes as follows. In Yan et
al., (1999), describes the characteristics of the micro-hole
of carbide by electric discharge machining with a copper
tool electrode. To achieve minimal expansion of the
machined micro-hole and minimal tool electrode wear
rate to secure a high precision micro-hole in the carbide,
the effects of changing the polarity, the tool electrode
shape, and the rotational speed of the tool electrode are
studied. Mahapatra and Amar Patnaik 2006, this work, it
is intended to study factors like discharge current, pulse
duration, pulse frequency, wire speed, wire tension and
dielectric flow rate and few selected interactions both for
maximizations of MRR and minimization of surface
roughness in WEDM process using Taguchi Method.
Cabanesa et al., 2006, discusses the results of the
analyses of an exhaustive experimental database that
reproduces unexpected disturbances that may appear
during normal operation. The results of the analyses
reveal new symptoms that allow one to predict wire
breakage. These symptoms are especially related to the
occurrence of an increase in discharge energy, peak
current, as well as increases and/or decreases in ignition
delay time. The differences observed in the symptoms
related to work piece thickness are also studied. Another
contribution of this paper is the analyses of the
distribution of the anticipation time for different validation
tests. Based on the results of the analyses; this paper
contributes to improve the process performance through
a novel wire breakage monitoring and diagnosing system
Mandal, et al., 2007, Artificial Neural Network (ANN) with
back propagation algorithm is used to model the process.
A multi-objective optimization method on-dominating
sorting genetic algorithm-II is used to optimize the
process. A large number of experiments have been
conducted with a wide range of current, pulse on time
and pulse off time. The MRR and tool wear have been
measured for each setting of current, pulse on time and
pulse off time. Sushant Dhar et al., 2007, Aluminum
Matrix Composites (AMC) are hard to machine due to the
presence of hard and brittle ceramic reinforcements.
EDM is an important process for machining such
materials. The present work evaluates the effect of
current (c), pulse-on time (p) and air gap voltage (v) on
Metal Removal Rate (MRR), Tool Wear Rate (TWR),
Radial Over Cut (ROC) on EDM of Al–4Cu–6Si alloy–10
wt.% SiCP composites. The optimum conditions for
maximum MRR with reduced TWR and ROC can also be
obtained using linear programming. The MRR, TWR and
ROC increase significantly in a nonlinear fashion with
increase in current.
144 Int. Res. J. Eng. Sci. Technol. Innov.
Table1. Different Variables Used in the Experiment and
Their Levels
Variable
1
Level
2
3
A
5
15
25
B
25
50
75
C
15
20
25
D
1
1.5
2
E
20
25
30
A
5
15
25
Coding
Discharge
current (Ip) in A
Discharge
Voltage in V
Pulse on time
(Ton)in µs
Pulse off Time
(Toff) in s
Oil Pressure in
2
kg/cm
Discharge
current (Ip) in A
EDM appears as an alternative to grinding and hard
turning for the machining of tool steels because EDM
allows the machining of any type of conducting material,
regardless of its hardness. Nevertheless, other factors
must be taken into accounting the selection of machining
processes, especially in the case of responsibility parts.
These factors are related with surface integrity: residual
stresses, hardness and structural changes generated by
the machining processes. In this work, the surface
integrity generated in AISIO1 tool steel by wire electrodischarge machining, hard turning and production
grinding is studied and compared. P. Asokan et al., 2008,
Owing to the complexity of ECM, it is very difficult to
determine optimal cutting parameters for improving
cutting performance. Hence, optimization of operating
parameters is an important step in machining, particularly
for unconventional machining procedures like ECM. In
this paper, current, voltage, flow rate and gap are
considered as machining parameters and metal removal
rate and surface roughness are the objectives by
applying grey relational analysis, they calculate the grey
grade for representing multi-objective.Sameh,2009,
highlights the development of a comprehensive
mathematical model for correlating the interactive and
higher order influences of various electrical discharge
machining parameters through Response Surface
Methodology (RSM), utilizing relevant experimental data
as obtained through experimentation. The mathematical
models have been developed on the basis of RSM,
utilizing the data from practical observable conditions of
the electrical discharge machining of work pieces.
Investigations were carried out for analysis of the control
conditions needed for the control of the material removal
rate, electrode wear ratio, gap size and surface
roughness. Seung-Han Yanga, et al., 2009, proposes an
optimization methodology for the selection of best
process parameters in EDM. Regular cutting experiments
are carried out on die-sinking machine under different
conditions of process parameters. This system model is
employed to simultaneously maximize the material
removal rate as well as minimize the surface roughness
using simulated annealing scheme. Muthu Kumar et at.,
2010, demonstrates optimization of WEDM process
parameters ofIncoloy800 super alloy with multiple
performance characteristics such as Material Removal
Rate (MRR), surface roughness and Kerf based on the
Grey–Taguchi Method. The process parameters
considered in this research work are Gap Voltage, Pulse
On-time, Pulse Off-time and Wire Feed by Krishna
Mohana Rao and Hanumantha Rao, 2010, work is aimed
at optimizing the hardness of surface produced in die
sinking EDM by considering the simultaneous affect of
various input parameters. The experiments are carried
out on Ti6Al4V, HE15, 15CDV6 and M-250 by varying the
peak current and voltage and the corresponding values of
hardness were measured.
A cylindrical pure copper with a diameter of 10 mm
was used as a tool electrode (of positive polarity) and
work piece materials used were AISI 1020 steel square
2
plates of surface dimensions 124×94mm and of
thickness 15 mm. Commercial grade EDM oil (specific
gravity = 0.763, freezing point= 94˚C) was used as
dielectric fluid with an electrode gap of 0.1mm. The test
conditions are given in the Table 1.
Response Surface Methodology
Response Surface Methodology (RSM) is a collection of
mathematical and statistical techniques that are useful for
modeling and analysis of problems in which output or
response influenced by several variables and the goal is
to find the correlation between the response and the
variables. It can be used for optimizing the response. It is
Rajesh and Anand 145
Table 2. Planning Matrix of the Experiments with the
Optimal Model Data
Sl.
No.
1.
2.
3.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
Ip
V
Ton
Toff
Poil
SR
15
25
25
25
25
5
25
15
25
25
15
15
15
5
15
5
5
5
5
15
15
5
15
15
25
5
15
15
5
25
15
15
50
75
75
75
25
25
75
50
50
25
50
50
75
75
50
50
25
25
75
50
50
75
50
50
25
25
25
50
75
25
50
50
25
25
25
15
15
15
15
20
20
25
15
20
20
15
20
20
25
15
25
20
20
15
20
20
15
25
20
20
25
25
20
20
1.5
2.0
1.0
1.0
1.0
2.0
2.0
1.5
1.5
1.0
1.5
1.0
1.5
1.0
1.5
1.5
1.0
1.0
1.0
1.5
1.5
2.0
1.5
1.5
2.0
2.0
1.5
1.5
2.0
2.0
1.5
2.0
25
30
20
30
20
20
20
25
25
30
25
25
25
20
30
25
20
30
30
25
25
30
25
25
30
30
25
20
20
20
25
25
2.75
2.73
2.70
2.96
2.90
0.81
2.60
3.10
3.49
2.90
2.70
3.30
2.95
2.20
3.06
2.45
1.38
1.22
2.50
3.10
3.10
2.60
3.09
3.10
2.96
0.98
2.41
2.97
2.60
2.90
3.10
3.20
an empirical modelization technique devoted to the
evaluation of relations existing between a group of
controlled experimental factors and the observed results
of one or more selected criteria. A prior knowledge of the
studied process is thus necessary to achieve a realistic
model. We selected only five experimental factors
capable of influencing the studied process yield: five
factors pulse current (Ip), pulse on time (Ton), pulse off
time (Toff), discharge voltage and Oil pressure.
The first step of RSM is to define the limits of the
experimental domain to be explored. These limits are
made as wide as possible to obtain a clear response from
the model. The pulse current (A), discharge voltage (B),
pulse on time (C), pulse off time (D) and Oil pressure (E)
are the machining variables, selected for our
investigation. The different levels retained for this study
are given in Table 1.
In the next step, the planning to accomplish the
experiments by means of response surface methodology
(RSM) using a Central Composite Design (CCD) with five
variables. Total numbers of experiments conducted with
the combination of machining parameter are presented in
Table 2. The central composite design used since it gives
a comparatively accurate prediction of all response
variable averages related to quantities measured during
experimentation. CCD offers the advantage that certain
146 Int. Res. J. Eng. Sci. Technol. Innov.
Table 3. ANOVA Table for SR Estimated Regression Coefficients
Term
Coefficient
Constant
A
B
C
D
E
A*A
B*B
C*C
D*D
E*E
A*B
A*C
A*D
A*E
B*C
B*D
B*E
C*D
C*E
D*E
-9.23635
0.17919
0.08620
0.70324
-2.29427
0.21678
-0.00114
-0.00065
-0.01438
0.66207
-0.00278
-0.00154
-0.00102
0.00050
0.00018
-0.00005
0.00460
0.00031
0.00100
-0.00425
-0.00100
SE
Coefficient
0.271665
0.004446
0.002094
0.017855
0.140269
0.021811
0.000106
0.000017
0.000426
0.042582
0.000426
0.000017
0.000083
0.000835
0.000083
0.000033
0.000334
0.000033
0.001670
0.000167
0.001670
T
P
-33.999
40.304
41.172
39.386
-16.356
9.939
-10.754
-37.995
-33.768
15.548
-6.527
-92.517
-12.276
0.599
2.096
-1.497
13.773
9.282
0.599
-25.450
-0.599
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.561
0.060
0.163
0.000
0.000
0.561
0.000
0.561
S = 0.0166996 R-Sq = 99.98% R-Sq(adj) = 99.94%
level adjustments are allowed and can be used in twostep chronological response surface methods. In these
methods, there is a possibility that the experiments will
stop with fairly few runs and decide that the prediction
model is satisfactory. Experiments have been carried out
on the EDM and the data were collected with respect to
the influence of the predominant process parameters on
SR. The 32 number of runs was conducted as per the
conditions of run are depicted in the Table 2.
The mathematical model is then developed that
illustrate the relationship between the process variable
and response. The behavior of the system is explained
by the following empirical second-order polynomial
model.
k
Y = β0 +
k
k
∑β Χ + ∑β X
i
i =1
i
ii
i =1
2
i
+
k
∑ ∑β X X
ij
i =1
i
j
i =1
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. The value
which are less then 0.05 are considered significant and
the values greater than 0.05 are not significant and the
model is adequate to represent the relationship between
machining response and the machining parameters. EDM
process is non-linear in nature the linear polynomial will
be not able to predict the response accurately therefore
the Second-order model (quadratic model) is used. It is
observed from the adequacy test by ANOVA those
linear terms Ip, V, Ton, Toff and Poil interaction terms Ip × V,
Ip × Ton, Ip × Poil, V × Toff, V × Poil and Ton × Poil and
square terms Ip2, V2, Ton2, Toff and Poil2 .The levels of
significant are depicted in the Table 3.The fit
summary recommended that the quadratic model is
statistically significant for analysis of SR. For the
appropriate fitting of SR, the no significant terms (p-value
is greater than0.05) are eliminated by backward
the elimination process. The ANOVA table for the
curtailed quadratic model for SR is shown in Table 4, the
reduced model results indicate that the model is
significant (R2 and adjusted R2 are 99.97% and 99.94%
respectively), and lack of fit is no significant (p-value is
less than 0.05). The final response equation for SR is
given as follows
SR = [-9.19010 + 0.17994 A + 0.08520 B + 0.70224 C 2.29177 D + 0.21528 E - 0.00114 A*A - 0.00065 B*B 0.01438 C*C + 0.66207 D*D - 0.00278 E*E - 0.00154
A*B - 0.00102 A*C + 0.00018 A*E + 0.00460 B*D +
0.00031 B*E - 0.00425 C*E]
The 32 experiments were conducted in duplicate and the
average values of SR With design matrix were tabulated
in Table 2. For analysis the data, the checking of
Rajesh and Anand 147
Table 4. ANOVA table for SR Estimated Regression Coefficients after
Backward Elimination
SE
T
P
Coefficient
Constant
-9.19010
0.250800
-36.643
0.000
A
0.17994
0.004168
43.176
0.000
B
0.08520
0.001938
43.951
0.000
C
0.70224
0.017194
40.842
0.000
D
-2.29177
0.126101
-18.174
0.000
E
0.21528
0.021168
10.170
0.000
A*A
-0.00114
0.000104
-11.008
0.000
B*B
-0.00065
0.000017
-38.891
0.000
C*C
-0.01438
0.000416
-34.564
0.000
D*D
0.66207
0.041601
15.915
0.000
E*E
-0.00278
0.000416
-6.681
0.000
A*B
-0.00154
0.000016
-94.699
0.000
A*C
-0.00102
0.000082
-12.565
0.000
A*E
0.00018
0.000082
2.145
0.049
B*D
0.00460
0.000326
14.098
0.000
B*E
0.00031
0.000033
9.501
0.000
C*E
-0.00425
0.000163
-26.050
0.000
S = 0.0163149 R-Sq = 99.97% R-Sq(adj) = 99.94%
Term
Coefficient
Table 5. Analysis of Variance for SR
Source
DF
Seq SS
Adj SS
Regression
Linear
Square
Interaction
16
5
5
6
13.5
6.59
4.27
2.68
13.5
2.94
4.27
2.68
Adj
MS
0.84
0.58
0.85
0.44
Residual Error
15
0.00
0.00
0.00
Lack-of-Fit
Pure Error
Total
10
5
31
0.00
0.00
13.5
0.00
0.00
goodness of fit of the model is very much required. The
model adequacy checking includes the test for
significance of the regression model, test for significance
on model coefficients, and test for lack of fit. For this
purpose, Analysis of Variance (ANOVA) is performed.
The fit summary recommended that the quadratic model
is statistically significant for analysis of SR.
RESULT AND DISCUSSION
The effect of the machining parameters (Ip,V, Ton, Toff and
Poil) on the response variables SR have been evaluated
by conducting experiments. The results are put into the
F
P
3183.2
2210.7
3210.5
1682.9
0.00
0.00
0.00
0.00
23.46
0.001
Minitab software for further analysis. The second-order
model was proposed in find the correlation between the
MRR and the process variables taken into account. The
analysis of variance (ANOVA) was used to check the
sufficiency of the second order model.
Figure (1 to 2) shows the estimated response surface
for SR in relation to the process parameters of Ip and Ton
while Toff, V and Poil remain constant at their lowest
values. It can be seen from the figure (1 to 2), the SR
tends to increase significantly with the increase in Ip for
any value of Ton. However, the SR tends to increase with
increase in Ton, especially at higher Ip. Hence,
minimum SR is obtained at low peak current (5A) and
low pulse on time (15µs). This is due to their dominant
148 Int. Res. J. Eng. Sci. Technol. Innov.
Contour Plot of SR vs Ton, Ip
25.0
SR
<
1.5 –
2.0 –
2.5 –
>
Ton
22.5
1.5
2.0
2.5
3.0
3.0
Hold Values
V
25
To ff
1
Po il
20
20.0
17.5
15.0
5
10
15
Ip
20
25
Figure 1. Effect of Surface Roughness Vs Pulse on Time and Discharge Current.
Contour Plot of SR vs Toff, Ip
2.0
SR
<
1.0 –
1.5 –
2.0 –
>
1.8
1.0
1.5
2.0
2.5
2.5
Ho ld Valu es
V
25
To n 15
Po il
20
T
off
1.6
1.4
1.2
1.0
5
10
15
Ip
20
25
Figure 2. Effect of Surface Roughness Vs Pulse off Time and Discharge Current.
Contour Plot of SR vs Toff, Ton
2.0
SR
<
0.8 –
1.0 –
1.2 –
>
1.8
0.8
1.0
1.2
1.4
1.4
Ho ld Values
Ip
5
V
25
Po il 20
T
off
1.6
1.4
1.2
1.0
15.0
17.5
20.0
Ton
22.5
25.0
Figure 3. Effect of Surface Roughness Vs Pulse off Time and Pulse on Time.
control over the input energy, i.e. with the increase in Ip
generates strong spark, which create the higher
temperature and crater, hence rough surface in the work
piece and low Ip creates small crater and therefore
smooth surface.
The effect of Ip and Toff is on the estimated response
surface of SR is depicted in Figure (2), Ton, V and Poil
remains constant in its lower levels. It can be noted that
the SR increases when the Ip increases, the explanation
is same, as stated earlier.
Figure (3 to 4) represents SR as a function of Ton and
Toff, whereas the Ip, V and Poil remains constant at its
lower levels. It is observed that the SR values are low
when Ton is low with higher Toff. Although the influence
Rajesh and Anand 149
Contour Plot of SR vs Ton, V
25.0
1.2
1.5
1.8
2.1
2.4
Ton
22.5
SR
<
–
–
–
–
–
>
1.2
1.5
1.8
2.1
2.4
2.7
2.7
Hold Values
Ip
5
Toff
1
Poil 20
20.0
17.5
15.0
30
40
50
V
60
70
Figure 4. Effect of Surface Roughness Vs Pulse on Time and Discharge Voltage.
Contour Plot of SR vs Toff, V
2.0
0.9
1.2
1.5
1.8
1.8
1.6
SR
<
–
–
–
–
>
0.9
1.2
1.5
1.8
2.1
2.1
Toff
Hold Values
Ip
5
Ton 15
Poil 20
1.4
1.2
1.0
30
40
50
V
60
70
Figure 5. Effect of Surface Roughness Vs Pulse off Time and Discharge Voltage.
Contour Plot of SR vs V, Ip
SR
<
1.5 –
2.0 –
2.5 –
>
70
V
60
1.5
2.0
2.5
3.0
3.0
Hold Values
Ton 15
Toff
1
Poil 20
50
40
30
5
10
15
Ip
20
25
Figure 6. Effect of Surface Roughness Vs Discharge Voltage and Discharge Current.
150 Int. Res. J. Eng. Sci. Technol. Innov.
Contour Plot of SR vs Poil, V
30
1.2
1.4
1.6
1.8
2.0
2.2
28
P
o
il
26
SR
<
–
–
–
–
–
–
>
1.2
1.4
1.6
1.8
2.0
2.2
2.4
2.4
Ho ld Values
Ip
5
To n
15
To ff
1
24
22
20
30
40
50
V
60
70
Figure 7. Effect of Surface Roughness Vs Oil Pressure and Discharge Voltage.
Contour Plot of SR vs Poil, Toff
30
0.8
0.9
1.0
1.1
28
26
SR
<
–
–
–
–
>
0.8
0.9
1.0
1.1
1.2
1.2
P
oil
Ho ld Values
Ip
5
V
25
To n 15
24
22
20
1.0
1.2
1.4
1.6
1.8
2.0
Toff
Figure 8. Effect of Surface Roughness Vs Oil Pressure and Pulse off Time.
Contour Plot of SR vs Poil, Ip
30
SR
<
1.5 –
2.0 –
2.5 –
>
28
1.5
2.0
2.5
3.0
3.0
Hold Values
V
25
Ton
15
Toff
1
Poil
26
24
22
20
5
10
15
Ip
20
25
Figure 9. Effect of Surface Roughness Vs Oil Pressure and Discharge Current.
of this two parameter is very less when compared with
the effect of Ip on SR.
Figure (7 to 9) represents SR as a function of Ton and V,
whereas the Ip, Toff and Poil remains constant at its lower
levels. It is observed that the SR values are low when Ton
and V are low.
CONCLUSIONS
The present study develops SR models for five different
parameters namely peak current, discharge time, pause
on time, pulse off time and oil pressure for EDM process
of AISI1020 steel using response surface method. The
Rajesh and Anand 151
second-order response models have been validated with
analysis of variance. It is found that all the five machining
parameters and some of their interactions have
significant effect on SR considered in the present study.
Finally, an attempt has been made to estimate the
optimum machining conditions to produce the best
possible SR within the experimental constraints.
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