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Optimization of EDM process parameters using Taguchi method
Conference Paper · February 2012
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International Conference on Applications and Design in Mechanical Engineering 2012 (ICADME 2012)
27-28 February 2012, Penang, Malaysia.
Optimization of EDM process parameters using
Taguchi method
1
Azizul Bin Mohamad, 2*Arshad Noor Siddiquee, 3Ghulam Abdul Quadir, 4Zahid A. Khan, 5V.K. Saini
1,3
School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Malaysia
2,4
Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, India
5
Department of Mechanical Engineering, IMS Engineering College, Ghaziabad, Uttar Pardesh, India.
1
azizul@unimap.edu.my, 2*arshadnsiddiqui@gmail.com, 3gaquadir@unimap.edu.my, 4zakhanusm@yahoo.com,
5
vksainig@gmail.com
Abstract— This paper presents investigation and optimization of
Electric Discharge Machining (EDM) parameters using Taguchi
method. Three process parameters chosen were Pulse on-time
(Ton), Duty factor and Discharge current (or pulse current). An
L27 orthogonal array was selected to study the effect of main
factors and interaction between factors on the response variable
i.e. surface roughness. Signal to Noise (S/N) ratios of the response
variable for all experiments were calculated. The contribution of
the main factors and interaction between them to the optimal
surface roughness were determined by using Analysis of
Variance (ANOVA). The experimental results revealed that
pulse-on time of 10 µs, duty factor of 7 and discharge current of
6 A yielded the optimal i.e. minimum surface roughness. Further,
results of ANOVA indicated that out of three main factors,
discharge current and out of three two way interactions,
interaction between duty factor and discharge Current as well as
interaction between pulse on-time and duty factor contributed
significantly in minimising the surface roughness.
Keywords- Taguchi method; Optimization; EDM; Surface
roughness; High strength low alloy steel
Introduction
Electric discharge machining (EDM) is one of the most
widely used non-traditional machining processes. This
technique utilises thermoelectric energy to erode undesired
materials from the work piece by a series of discrete electrical
sparks between the work piece and the electrode. A pulse
discharge occurs in a small gap between the work piece and
the electrode which removes the unwanted material from the
parent metal through melting and vaporizing. The electrode
and the work piece must have electrical conductivity in order
to generate the spark. The process uses thermal energy to
generate heat that melts and vaporizes the work piece by
ionization within the dielectric medium. The electrical
discharges generate impulsive pressure by dielectric explosion
to remove the melted material. Thus, the amount of removed
material can be effectively controlled to produce complex and
precise machine components. However, the melted material is
flushed away incompletely and the remaining material
resolidifies to form discharge craters. As a result, machined
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surface has micro cracks and pores caused by high
temperature gradient which reduces surface finish quality.
There have been many published studies considering surface
finish of machined materials by EDM. It was noticed that
various machining parameters influenced surface roughness
and setting possible combination of these parameters was
difficult to produce optimum surface quality. This paper
presents optimization of EDM process parameter.
Investigations have indicated that the output parameters of
EDM increased with the increase in pulsed current and the
best machining rates were achieved with copper and
aluminium electrodes [1]. Surface roughness has an increasing
trend with an increase in the discharge duration. This is mainly
due to more discharge energy released during this time and
expanding the discharge channel [2]. Significant electrode
wear was observed in EDM process in which DIN 1.2714 tool
steel and on-time current were used. Experimental results have
indicated that the EDM process caused a ridged surface and
induced machining damage in the surface layer, and increased
the surface roughness [3]. Increasing the pulse current and
reducing the pulse-on duration provides an effective means of
suppressing the surface cracking phenomenon [4]. Higher
values of the pulse current and pulse-on duration are found to
increase the average thickness of the recast layer [5].
Machining characteristics of EN-8 steel were investigated and
empirical models were developed for prediction of output
parameters viz MRR, TWR and surface roughness using linear
regression analysis [6]. Analysed results yielded that peak
current and pulse on time were the most significant parameters
for MRR and TWR respectively. But peak current and
electrode rotation were the most significant and parameters for
surface roughness [6]. The effects of electrode tool materials
and machining input parameters such as current, pulse-on time
on AISI D3 EDM characteristic was studied and reported that
the graphite electrode, having highest material removal rate
and precise dimension and low tool wear ratio, is the most
appropriate material for steel machining [7].
I. TAGUCHI METHOD
Taguchi method, developed by Dr. Genichi Taguchi, is a
set of methodologies for optimization of a process or product.
The application of this technique has become widespread in
many US and European industries after the 1980s. This
method involves three stages: system design, parameter
design, and tolerance design. Out of these three stages, the
second stage – the parameter design – is the most important
stage as the first stage – system design – is an initial functional
design and may be far from quality and cost. However, the
third stage – tolerance design – is dependent of cost.
Therefore, the parameter design is the key step in the Taguchi
method to achieving high quality without increasing cost.
Originally, Fisher was developer of classical experimental
design but it is difficult to use mainly due to two reasons, first
complexity, second, it needs the large number of experiments
if number of the process parameters increases. This task was
simplified by Taguchi by introducing a special design of
orthogonal arrays to study the entire parameter space with a
small number of experiments only and thus, it results in a lot
of cost as well as time saving. In the Taguchi method, the
experimental values are transformed into a signal-to-noise
(S/N) ratio η. The term “signal” represents the desirable value
(mean) for output characteristic and the term “noise”
represents the undesirable value for the output characteristic.
Usually there are three categories of the performance
characteristic in the analysis of the S/N ratio, that is, the
lower-the-better, nominal-the-better and the higher-the-better.
The S/N ratio for each level of process parameters is
computed based on the S/N analysis. Regardless of the
category of the performance characteristic, the larger S/N ratio
corresponds to the better performance characteristic.
Therefore, the optimal level of the process parameters is the
level having highest S/N ratio. Furthermore, statistical analysis
of variance (ANOVA) is performed to see which process
parameters are statistically significant. The optimal
combination of the process parameters can be predicted by
S/N and ANOVA analyses. Finally, a confirmation experiment
is conducted to verify the optimal process parameters obtained
from the parameter design. In this study, the parameter design
of Taguchi method is adopted to obtain optimal machining
performance in CNC milling.
The S/N ratios are expressed on a decibel scale. Factor
levels that have maximum S/N ratio are considered as optimal.
The aim of this study was to produce minimum surface
roughness (Ra) in an EDM machining operation. Smaller-thebetter quality characteristic is used for surface roughness as
smaller Ra values represent better or improved surface
roughness. Optimization of quality characteristics using
parameter design of the Taguchi method are summarized in
the following steps:
(1)
Quality characteristics and process parameters are
identified and evaluated;
(2)
Identification of number of levels for the process
parameters and possible interactions between the
process parameters;
(3)
Assignment of process parameters to the selected
appropriate orthogonal array;
(4)
Conduction of experiments based on the arrangement
of the orthogonal array;
(5)
Calculation of S/N ratio;
(6)
Analyze the experimental results using the S/N ratio
and ANOVA analysis;
(7)
Selection of
the optimal levels of process
parameters;
(8)
Verification of the optimal process parameters
through the confirmation experiment.
II. EDM MACHINING EXPERIMENTS
EDM is one of the earliest non-traditional machining
processes. EDM process is based on thermoelectric energy
between the work piece and an electrode. A pulse discharge
occurs in a small gap between the work piece and the
electrode and removes the unwanted material from the parent
metal through melting and vaporizing. The electrode and the
work piece must have electrical conductivity in order to
generate the spark.
Smaller-is-better:
Nominal-is-better:
Larger Nominal-is-best:
Fig. 1 Electric discharge machining set up
Where, yij is the ith experiment at the jth test, n is the total
test and s is the standard deviation.
A. Selection of cutting parameters and their levels
In this study, an EDM machine (Electronica) was used to
perform experiments. Pure copper rod was used for an
electrode. The experimental set-up is shown in Fig. 1. The
work piece and electrode were separated by a moving
dielectric fluid i.e. kerosene. The material chosen for work
piece was High Strength Low Alloy (HSLA) steel. HSLA
steel is a type of steel alloy that provides many benefits over
regular steel alloys. In general, HSLA alloys are much
stronger and tougher than ordinary plain carbon steels. They
are used in cars, trucks, cranes, bridges and other structures
that are designed to handle a lot of stress, often at very low
temperatures. HSLA steels are so called because they only
contain a very small percentage of carbon.
iii. Flooding the volume (work tank) around work piece by
flushing with the dielectric fluid, up to the height where
the electrode sparking region is fully immersed.
iv. Perform the machining process for a pre-decided time
interval, which is 4 minutes. Intermittent sparking is
visible through the dielectric fluid. A large number of
current discharges happen, each contributing to the
removal of material from both tool and work piece,
where small craters are formed. Erosion from the
electrode is termed as wear, while that from the work
piece is the desired machining.
v. The dielectric fluid is drained back into the dielectric
tank, present below the work table.
vi. The work-piece is taken off from the machine and its
surface roughness (SR) is checked. The readings are
carefully noted down.
C. Machining performance measure
Fig. 2. CNC Grinder used to give a smooth finish to surface
of the HSLA steel work piece
HSLA steel work piece was originally an L-shaped plate
having the dimensions of 85 cm × 45 cm × 1.3 cm. This was
cut down to 12cm × 4cm × 1.3 cm size pieces by using the
Shearing machine. The surface to be machined needs to be
smooth and free from any sort of corrosion or scaling. This
was achieved by grinding the surface using a CNC Grinder
(Fig. 2).
Machining experiments for determining the optimal
machining parameters were carried out by setting: For each
experiment the combinations of the 3 input parameters viz.
Pulse on-time (A) in the range of 10 µs to 300 µs, Duty Factor
(B) in the range of 4 to 10, Discharge Current (C) in the range
of 1.5A to 6A, all having 3 levels (Table 1).
Symbol
Out of various surface finish parameters i.e. roughness
average (Ra), root-mean-square (rms) roughness (Rq), and
maximum peak-to-valley roughness (Ry or Rmax), the
parameter Ra, which is most widely used in industry, was
selected in this study. Different settings of Pulse on-time, Duty
Factor, and Discharge Current (Table 2) were used to conduct
the experiments. The surface roughness of all the specimens
was measured using the Taylor-Hobson Surf Com instrument
for a sampling length of 5 mm, as per the recommendations of
ASME B-46.1-2002.
TABLE 1
FACTORS AND LEVELS USED IN EXPERIMENT
III. DETERMINATION OF OPTIMAL CUTTING PARAMETERS
In this section, the use of an orthogonal array to reduce the
number of cutting experiments for determination of optimal
cutting parameters is presented. Results of the cutting
experiments are studied by using the S/N and ANOVA
analyses. Based on the results of these analyses, optimal
cutting parameters for minimum surface roughness are
obtained and verified.
Machining
parameter
A.
A
Pulse
time
on-
B
Duty Factor
C
Discharge
Current
Unit
µs
A
Level 1
Level 2
Level 3
10
150
300
4
7
10
1.5
4
6
B. Steps involved in carrying out experiment
Experiment involved the following steps:
i. Fixing the electrode in position in the ram hold. Its
height was auto-adjusted by machine with respect to the
work piece to maintain a very small gap of 50 µm
between its tip and the work piece surface.
ii. Fixing the work piece in position on the magnetic chuck
of the machine’s work-table.
Orthogonal array experiment
In order to select an appropriate orthogonal array for
experiments, the total degrees of freedom need to be
computed. The degrees of freedom are defined as the number
of comparisons between process parameters that need to be
made to determine which level is better and specifically how
much better it is. For example, a three-level process parameter
counts for two degrees of freedom. The degrees of freedom
associated with interaction between two process parameters
are given by the product of the degrees of freedom for the two
process parameters. In this study, the interaction between the
cutting parameters is also playing important role. Therefore,
there are two degrees of freedom for each process parameters
(Pulse on-time, Duty Factor and Discharge Current) and four
degree of freedom for each interaction of parameters (A×B,
A×C, B×C).
TABLE 2
EXPERIMENTAL LAYOUT USING AN L27 ORTHOGONAL ARRAY
L27 orthogonal array columns
Run
2
5
6
LEVEL OF CONTROL
PARAMETERS
(A)
(B)
(C)
7
8
9
INTERACTIONS
B×C
A×B
A×C
1
1
1
1
1
1
1
2
1
2
2
2
2
2
3
1
3
3
3
3
3
4
2
1
1
1
2
2
5
2
2
2
2
3
3
6
2
3
3
3
1
1
7
3
1
1
1
3
3
8
3
2
2
2
1
1
B.
Analysis of the signal-to-noise (S/N) ratio
The S/N equation depends on the criterion for the quality
characteristic to be optimized. There are three categories of
performance characteristics, i.e., the lower-the-better,
nominal-the-better and the higher-the-better. In this study, the
lower-the-better performance characteristic is selected to
obtain minimum surface roughness. The experimental results
for surface roughness and the corresponding S/N ratio using
equation (1) are shown in Table 3.
TABLE 3
L27 MACHINING ORTHOGONAL ARRAY WITH THE VALUES OF
RESPONSE VARIABLES
run
LEVEL OF CONTROL
PARAMETERS
Measured
response
parameter
Surface
Roughness
(SR) (µm)
S/N
Ratio
9
3
3
3
3
2
2
10
1
1
2
3
1
2
1
Pulse
on
Time
(A)
10
11
1
2
3
1
2
3
2
10
7
4
1.7841
-5.0284
12
1
3
1
2
3
1
3
10
10
6
1.4583
-3.2769
13
2
1
2
3
2
3
4
150
4
1.5
1.8879
-5.5196
14
2
2
3
1
3
1
5
150
7
4
1.7858
-5.0367
15
2
3
1
2
1
2
6
150
10
6
1.5638
-3.8836
16
3
1
2
3
3
1
7
300
4
1.5
1.5078
-3.5669
17
3
2
3
1
1
2
8
300
7
4
1.6653
-4.4298
18
3
3
1
2
2
3
9
300
10
6
1.7998
-5.1045
19
1
1
3
2
1
3
10
10
4
4
1.5058
-3.5553
20
1
2
1
3
2
1
11
10
7
6
1.4469
-3.2088
21
1
3
2
1
3
2
12
10
10
1.5
1.9500
-5.8007
22
2
1
3
2
2
1
13
150
4
4
1.6523
-4.3618
23
2
2
1
3
3
2
14
150
7
6
1.5396
-3.7482
24
2
3
2
1
1
3
15
150
10
1.5
1.7417
-4.8195
25
3
1
3
2
3
2
16
300
4
4
1.5618
-3.8725
26
3
2
1
3
1
3
17
300
7
6
1.5835
-3.9924
27
3
3
2
1
2
1
18
300
10
1.5
1.7429
-4.8254
19
10
4
6
1.6413
-4.3038
20
10
7
1.5
1.6454
-4.3254
21
10
10
4
1.6211
-4.1962
22
150
4
6
1.7379
-4.8005
23
150
7
1.5
1.6604
-4.4043
24
150
10
4
1.5823
-3.9858
25
300
4
6
1.6171
-4.1747
26
300
7
1.5
1.6043
-4.1057
27
300
10
4
1.6180
-4.1796
Once the degrees of freedom are evaluated, the appropriate
orthogonal array is selected to serve the specific purpose.
Basically, the degrees of freedom for the orthogonal array
should be greater than or at least equal to those for the process
parameters. In this study, an L27 orthogonal array was used.
The lower is the SR in the EDM process, the better is the
machining performance. For each experiment the combination
of the 3 input parameters viz. Pulse on-time (A), Duty Factor
(B), and Discharge Current (C), all having 3 levels, is changed
according to the experimental plan in L27 orthogonal array.
The experimental layout for the three cutting parameters using
the L27orthogonal array is shown in Table 2.
Duty
Factor
(B)
Discharge
Current
(C)
SR
4
1.5
1.7819
-5.0177
Since the experimental design is orthogonal, it is then
possible to separate out the effect of each cutting parameter at
different levels. For example, the mean S/N ratio for the speed
at levels 1, 2 and 3 can be calculated by averaging the S/N
ratios for the experiments (1–3, 10-12 & 19-21), (4-6, 13-15 &
22-24) and (7-9, 16-18 & 25-27) respectively. The mean S/N
ratio for each level of the other cutting parameters and their
interaction can be computed in the similar manner. The mean
S/N ratio for each level of the cutting parameters is
summarized and called the mean S/N response table for
surface roughness (Table 4).
TABLE 4
RESPONSE TABLE FOR AVERAGE S/N RATIO FOR SURFACE ROUGHNESS
FACTORS AND SIGNIFICANT INTERACTION
Control factors
Where is number of experiments in the orthogonal array,
is the mean S/N ratio for the th experiment. CF is correction
factor. If we want to calculate sum of square for factor A can
be calculated by:
where L = Number of levels,
ni, nk = number of test sample at levels Ai and Ak respectively,
T2/n = correction factor (CF)
Interactions
Levels
A
B
C
A×B
A×C
B×C
1
-3.65
-3.78
-3.88
-3.48
-3.73
-3.43
2
-3.88
-3.69
-3.85
-3.99
-3.90
-4.44
3
-3.78
-3.84
-3.58
-3.84
-3.68
-3.43
TABLE 5
RESULTS OF THE ANOVA FOR SURFACE ROUGHNESS
Interaction effects are always mixed with the main effects
of the factors assigned to the column designated for interaction.
Symbol
Fig.2 shows the mean S/N ratio graph for surface
roughness. The S/N ratio corresponds to the smaller variance
of the output characteristics around the desired value. From
Table 3, the overall mean for the S/N ratio of SF found to be
−3.77. Figures 3 show graphically the effect of the three
control factors and their interactions on SF. Analysis of the
result leads to the conclusion that factors at level A1 , B2, C3,
gives best SR.
DF
SS
MS
F
P
Ratio
A
Pulse ontime
2
0.0029
0.0015
1.33
2.59
B
Duty
Factor
2
0.0013
0.0006
0.58
1.14
C
Discharge
Current
2
0.0060
0.0030
2.73
5.32
A×B
Interaction
Between
Pulse ontime &
Duty factor
4
0.0149
0.0037
3.39
13.20
A×C
Interaction
Between
Duty factor
&
Discharge
Current
4
0.0029
0.0007
0.66
2.55
B×C
Interaction
Between
Duty factor
&
Discharge
Current
4
0.0760
0.0190
17.31
67.41
Error
8
0.0088
0.0011
Total
26
0.113
Main effect plot for Mean S/N ratio
-2.60
-2.85
Machining
Parameter
-3.10
Mean
-3.35
-3.60
-3.85
-4.10
-4.35
-4.60
B1 B2 B3 C 1 C 2 C 3
A × A× A× A × A × A ×
A1
A2
A3
B1
B2
B3 ×B1 ×B2 ×B3
C C C
Cutting parameter level
C1
C2
C3
Fig. 3 The smaller the better S/N graph for surface roughness.
C.
Analysis of variance (ANOVA)
The purpose of ANOVA was to investigate which machining
parameters
significantly
affected
the
performance
characteristics. This was accomplished by separating the total
variability of the S/N ratios, which is measured by the sum of
the squared deviations from the total mean of the S/N ratio,
into contributions by each of the process parameters and the
error. First, the total sum of the squared deviations SST from
the total mean of the S/N ratio is calculated using:
7.79
100.0
DF - degrees of freedom, SS - sum of squares, MS - mean
squares(Variance), F-ratio of variance of a source to variance of
error, P- % Contribution
The relative significance of interaction effects is obtained by
ANOVA just as are the relative significance of factor effects.
Statistically, there is a tool called the
test to see which
process parameters have significant effect on the quality
characteristic. For performing the
test, the mean of
squared deviations MS due to each process parameter needs to
be calculated. The mean of squared deviations SSm is equal to
the sum of squared deviations SS divided by the number of
degrees of freedom associated with the process parameter.
Then, the value for each process parameter is simply the
ratio of the mean of squared deviations MS to the mean of
squared error SSe. Parameter C i.e. Discharge Current with a
contribution of 5.32% has the greatest effect on the machining
output characteristics. Parameter A i.e. Pulse on-time (Ton),
with a 2.59% share is the next most significant influence on
the output parameters, followed by Parameter B i.e. machine’s
Duty factor (1.14%). It is most interesting to note from the
above analysis that the SR of work piece in EDM process is
most affected by the interaction of parameters B and C. Its’
contribution of about 67.41% is the single-largest. The
contribution of parameters A and B on the SR is 13.20%.
Surface finish quality was better when applying smaller
pulse time. This is because of small particle size and crater
depths formed by electrical discharge. As a result, the best
surface finish will be produced. The selection of these
machining parameters for EDM of any material should be
used for a higher surface quality is required. It was observed
that when Discharge current and particularly pulse on time
increased, machined work piece surface exhibited a higher
surface roughness due to irregular topography. Discharge
current had an effect on surface roughness at low pulse time,
but the influence of pulse time was more significant than
Discharge current at higher pulse times. It was noticed that
high Discharge current and pulse times will produce a poor
surface finish due to deeper and wider crates on the machined
surface. Excellent machined surface quality could be obtained
by setting machining parameters at a low short pulse time.
IV. CONFIRMATION TEST
After obtaining the optimal level of the process parameters,
the next step is to verify the percentage change of surface
roughness between predicted and experimental value for this
optimal combination. Table 6 compares the results of the
confirmation experiments using the optimal slab milling
process parameters (A1B2C3) obtained by the proposed method
and the initial machining parameters (A1B1C1). As shown in
Table 6, that S/N ratio improved from -5.0177 to -3.209 (an
improvement of 36.04.08%) and, therefore, the surface
roughness value is improved by 1.2 times. In other words, the
experiment results confirm the prior design and analysis for
optimizing the machining parameters. Surface roughness in
EDM operations are greatly improved through the approach.
Optimal machining parameters
Level
S/N ratio
Surface
roughness (μm)
1.7819
Prediction
Experiment
A1B2C3
-2.214
A1B2C3
-3.209
1.2903
1.4469
V. CONCLUSIONS
This research work has presented an investigation on the
optimization and the effect of machining parameters on the
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The following factor-level settings have been identified to
yield the best combination:
Input parameter A – Level 1
Input parameter B – Level 2
Input parameter C – Level 3
The level of importance of the machining parameters & their
individual contributions on the surface roughness is
determined by using Analysis of Variance (ANOVA). The
parameter C (discharge current or pulse current, IP) was found
to be most effective on surface roughness, followed by
parameter A (pulse on-time, Ton) which is almost half as
effective as parameter C. Control (input) parameter B (Duty
factor) was found to be least influencing the machining
process quality. The single largest significance for process
quality is of the interaction between parameters B and C (B ×
C) with about 67.41% contribution.
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
TABLE 6
RESULTS OF CONFIRMATION TEST
Initial Cutting
Parameters
A1B1C1
-5.0177
electrode surface roughness in EDM operations. The input
parameters chosen were Pulse on-time (Ton), Duty factor and
Discharge current (or pulse current). The L27 orthogonal array
was chosen to be able to study the effect of mutual interaction
between control (input) parameters on the SR. S/N (Signal to
Noise) ratios for all experiments are calculated corresponding
to SR. Machining parameters set at their optimum levels can
ensure significant improvement in the response functions and
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