1. International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383
(Print), ISSN 0976 – 6391 (Online) Volume 4, Issue 1, January – April (2013), © IAEME
39
EXPERIMENTAL INVESTIGATION OF MACHINING PARAMETERS
OF ELECTRIC DISCHARGE MACHINE ON TUNGSTEN CARBIDE
(K-10)
Er. RAVINDER KHANNA1
, Er. SUMIT GARG2
1
(Mechanical Engineering, PCET, Lalru/Punjab Technical University, Jalandhar)
2
(Mechanical Engineering, PCET, Lalru/Punjab Technical University, Jalandhar)
ABSTRACT
Machining of hard metal is very difficult process by conventional method. So we use
a non conventional method for hard material the method is known as electrical discharge
machining (EDM) process. And the material for machining is tungsten carbide (k-10). Where
the composition of that material is (94% of W & 6% of C)? Electrical discharge machining
(EDM) is one of the most widely used non-traditional machining processes. Electrical
Discharge Machining (EDM) is the process of machining electrically conductive materials by
using precisely controlled sparks that occur between an electrode and a workpiece in the
presence of a dielectric fluid. The electrode may be considered the cutting tool. This paper
investigate about the effect of EDM parameters using pulse on time, pulse of time, current
and voltage on material removal rate (MRR) . In the present Work we take copper as
electrode. Using Taguchi method, an L16 orthogonal array is used in the experiment and four
levels corresponding to each of the variables are taken.
Keywords: EDM, metal removal rate (MRR), Taguchi method, Tungsten Carbide (WC)
1. INTRODUCTION
Electrical Discharge Machining (EDM) is a well-known machining technique since
more than fifty years. Nowadays it is the most widely-used non-traditional machining
process, mainly to produce injection molds and dies, for mass production of very common
objects. It can also produce finished parts, such as cutting tools and items with complex
shapes. EDM is used in a large number of industrial areas: automotive industry, electronics,
domestic appliances, machines, packaging, telecommunications, watches, aeronautic, toys,
INTERNATIONAL JOURNAL OF PRODUCTION TECHNOLOGY AND
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ISSN 0976- 6383 (Print)
ISSN 0976 - 6391 (Online)
Volume 4, Issue 1, January - April (2013), pp. 39-45
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2. International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383
(Print), ISSN 0976 – 6391 (Online) Volume 4, Issue 1, January – April (2013), © IAEME
40
surgical instruments. The principle of EDM is to use the eroding effect of controlled electric
spark discharges on the electrodes. It is thus a thermal erosion process. The sparks are created
in a dielectric liquid, generally water or oil, between the workpiece and an electrode, which
can be considered as the cutting tool. There is no mechanical contact between the electrodes
during the whole process. Since erosion is produced by electrical discharges, both electrode
and workpiece have to be electrically conductive. Thus, the machining process consists in
successively removing small volumes of workpiece material, molten or vaporized during a
discharge. The volume removed by a single spark is small, in the range of 10-6
-10-4
mm3
, but
this basic process is repeated typically 10’000 times per second. Electrical discharge
machining (EDM) is a process that is used to remove metal through the action of an electrical
discharge of short duration and high current density between the tool and the workpiece [1-
2].
Both tool and work piece are submerged in a dielectric fluid .Kerosene/EDM
oil/deionized water is very common type of liquid dielectric although gaseous dielectrics are
also used in certain cases. Tungsten carbide (WC) is an important tool and dies material,
mainly because of its high hardness, strength and wears resistance over a wide range of
temperatures. It has a high specific strength and cannot be easily processed by conventional
machining techniques. Tungsten carbide is a type of cemented carbide; the particles of
carbide are bound by the process of powder metallurgy [3-5] to produce tungsten carbide
(WC).
2. EXPERIMENTATION PROCESS
A number of experiments were conducted to study the effects of various machining
parameters on EDM process. These studies have been undertaken to investigate the effects of
current (Ip), pulse on time (Ton), pulse off Time (Toff) and voltage (v).
The selected work piece material for the work is tungsten carbide. The tungsten carbide has
(94% of W & 6% of C).The material for the electrode is copper. Have a diameter of 6mm.
Experiments are conducted on brand Elektra model number es 5535 Die Sinking Machine.
Physical Properties of Tungsten Carbide
Properties
Melting
point
Density
Thermal
expansion
Hardness
Elastic
modulus
Tungsten
carbide
(Wc)
2,800 Oc
14.5 g/cm3 5×10-6
Oc
93.7 (HRA) 648 Gpa
In the experiment we calculate the MRR and for the calculation of MRR we have to measure
the weight of workpiece after every run of experiment. Every time the material is removed
from the workpiece due to heat generated by the arc, the remove debris from the workpiece
as a result of that the weight of the workpiece decreases. to measure the initial and final
weight of workpiece.

3. International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383
(Print), ISSN 0976 – 6391 (Online) Volume 4, Issue 1, January – April (2013), © IAEME
41
3. DESIGN OF EXPERIMENTS AND ANALYSIS
3.1 Design of Experiments
The experimental layout for the machining parameters using the L16 orthogonal array was
used in this study.
Table no. 3.3.1 Input Machining Parameters
S.NO
.
INPUT
PARMETERS
LEVEL OBSERVED VALUE
1 2 3 4 Material removal rate
(MRR)
In
g/min.
1. Current (Ip) 10 13 17 20
2. Pulse on time (Ton) 50 100 150 200
3. Pulse off time (Toff) 3 6 9 12
4. Voltage(V) 20 30 40 50
This array consists of four control parameters and four levels, as shown in table no. 3.3.1. In
the taguchi method, most all of the observed values are calculated based on ‘higher is the
better’. Each experimental trial was performed with three simple replications at each set
value. Next, the optimization of the observed values was determined by comparing the
standard analysis and analysis of variance (ANOVA) which was based on the taguchi
method.
3.2 Results and analysis of MRR for machined surface
The effect of parameters i.e. Current, Pulse on time, Pulse off time and Voltage some
of their interactions were evaluated using ANOVA. A confidence interval of 95% has been
used for the analysis. To measure Signal to Noise ratio (S/N ratio) calculated by the formula
(S/N) HB = -10 log (MSDHB)
For material removal rate (MRR) the S/N ratio is larger is better.
Where MSDHB = Mean Square deviation for higher the better response.
3.3 Observation table
During the conduction of all the 16 experiments with different set of input parameters
observation were made for the weight lost in the gram from the workpiece in each experiment
and time taken in the minute for the machining of each experiment was also observed. For all
the readings time taken was 1 hour. After the completion of all the experiments the
observation reading of the weight loss and time taken were filled in orthogonal array as
shown in table 3.3.1

4. International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383
(Print), ISSN 0976 – 6391 (Online) Volume 4, Issue 1, January – April (2013), © IAEME
42
Table no. 3.3.1 Average Table for MRR for Material
SR
NO.
Ip
(A)
Ton
(µs)
Toff
(µs)
Voltag
e
(V)
MRR
(gm/min)
S/N
Ratio
MEAN
Ratio
M1 M2 M3
1 10 50 3 20
0.0015 0.0013 0.0013 -57.2867 0.0013667
2 10 100 6 30
0.0047 0.0028 0.0016 -50.3616 0.0030333
3 10 150 9 40
0.0023 0.0016 0.0038 -51.8126 0.0025667
4 10 200 12 50
0.0035 0.0025 0.0033 -50.1728 0.0031
5 13 50 6 40
0.0047 0.0031 0.0045 -47.7443 0.0041
6 13 100 3 50
0.0095 0.0075 0.0066 -42.0842 0.0078667
7 13 150 12 20
0.0023 0.0016 0.002 -54.1254 0.0019667
8 13 200 9 30
0.002 0.003 0.0028 -51.7005 0.0026
9 17 50 9 50
0.0078 0.0063 0.0083 -42.5375 0.0074667
10 17 100 12 40
0.0025 0.0043 0.0045 -48.4809 0.0037667
11 17 150 3 30
0.003 0.0046 0.0035 -48.636 0.0037
12 17 200 6 20
0.0025 0.0016 0.0021 -53.6946 0.0020667
13 20 50 12 30
0.0028 0.0053 0.0045 -47.535 0.0042
14 20 100 9 20
0.0031 0.0033 0.0037 -49.456 0.0033667
15 20 150 6 50
0.0068 0.008 0.009 -42.0109 0.0079333
16 20 200 3 40
0.0037 0.0048 0.0055 -46.6199 0.0046667
3.4 Analysis of variance – MRR:
The results were analyzed using ANOVA for identifying the significant factors
affecting the performance measures. The Analysis of Variance (ANOVA) for the mean MRR
at 95% confidence interval is given in Tables. The variation data for each factor and their
interactions were F-tested to find significance of each calculated by the formula. The
principle of the F-test is that the larger the F value for a particular parameter, the greater the
effect on the performance characteristic due to the change in that process parameter. ANOVA
table shows that current, pulse on time, pulses off time, voltage are the factors that
significantly affect the MRR. Voltage has highest contribution to MRR. Main effect plot for
the mean MRR is shown in the graph which shows the variation of MRR with the input
parameters. As can be seen MRR increases with increase in voltage from 20v to 50v.
3.5 Conformation test
From mean of each level of every factor we will construct response table for MRR is
given below:

5. International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383
(Print), ISSN 0976 – 6391 (Online) Volume 4, Issue 1, January – April (2013), © IAEME
43
Table no. 3.5.1 Response Table for Signal to Noise Ratios (Larger is Better)
LEVEL
CURRENT
(A)
Pulse on time
(B)
Pulse off time
(C)
Voltage
(D)
1 -52.41 -48.78 -48.66 -53.64
2 -48.91 -47.60 -48.45 -49.56
3 -48.34 -49.15 -48.88 -48.66
4 -46.41 -50.55 -50.08 -44.20
Delta 6.00 2.95 1.63 9.44
Rank 2 3 4 1
Table no. 3.5.2Response Table for Means
Level Current Pulse on time Pulse off time Voltage
1 0.002517 0.004283 0.004400 0.002192
2 0.004133 0.0045080 0.004283 0.003383
3 0.004250 0.004042 0.004000 0.003775
4 0.005042 0.003108 0.003258 0.006592
Delta 0.002525 0.001400 0.001142 0.004400
Rank 2 3 4 1
From above main effect plot of MRR we can conclude the optimum condition for MRR is
A4, B2, C2, D4 i.e. Current (20amp.), Pulse-on (100µs) and Pulse-off (6 µs), Voltage (50V).
4321
-45.0
-47.5
-50.0
-52.5
-55.0
4321
4321
-45.0
-47.5
-50.0
-52.5
-55.0
4321
A
MeanofSNratios
B
C D
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better
4321
0.006
0.005
0.004
0.003
0.002
4321
4321
0.006
0.005
0.004
0.003
0.002
4321
A
MeanofMeans
B
C D
Main Effects Plot for Means
Data Means
Main Effect Plot for S/N Ratio of MRR Main Effect Plot for Means of MRR
The table showing the s/n ratio of MRR where we check the value of p which is less than of
0.05 and in the table 0.023 for current and 0.007 of voltage .which have been showing
voltage factor have more contribution for removing the material after that current have been
giving there contribution for that.

6. International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383
(Print), ISSN 0976 – 6391 (Online) Volume 4, Issue 1, January – April (2013), © IAEME
44
Table no. 3.5.3 ANOVA for S/N Ratio of MRR
Source DF Seq SS Adj SS Adj MS F P
Current 3 75.179 75.179 25.060 16.34 0.023
Pon 3 17.743 17.743 5.914 3.86 0.148
Poff 3 6.378 6.378 2.126 1.39 0.397
Voltage 3 179.945 179.945 59.982 39.12 0.007
Residual
error
3 4.600 4.600 1.533
Total 15 283.845
Table no. 3.5.4 ANOVA for Mean Ratio of MRR
Source DF Seq SS Adj SS Adj MS F P
Current 3 0.0000135 0.0000135 0.0000045 11.04 0.040
Pon 3 0.0000045 0.0000045 0.0000015 3.72 0.154
Poff 3 0.0000032 0.0000032 0.0000011 2.59 0.228
Voltage 3 0.0000417 0.0000417 0.0000139 34.18 0.008
Residual
error
3 0.0000012 0.0000012 0.0000004
Total 15 0.0000640
** Significant at 95% confidence level
Seq SS= Sum of squares, DOF= degree of freedom, Adj MS= adjusted mean square or
variance.
3.6 Optimal design for MRR
In the experimental analysis, main effect plot of S/N ratio is used for estimating the
S/N ratio of MRR with optimal design condition. As shown in the graphs, there are highest
values which effect the material removal rate which are the current (A4), pulse-on (B2), pulse
off (C2) and voltage (D4) respectively. 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. The estimated S/N ratio using the
optimal level of the design parameters can be calculated:
nopt = -49.016 + ( -46.41+49.016) + (-47.60+49.016) + (-48.45+49.016) +(-44.20+49.016)
= -39.6114

7. International Journal of Production Technology and Management (IJPTM), ISSN 0976 – 6383
(Print), ISSN 0976 – 6391 (Online) Volume 4, Issue 1, January – April (2013), © IAEME
45
y2
opt =
ଵ
ଵ଴
ష೙೚೛೟
భబ
y2
opt =
ଵ
ଵ଴
యవ.లభభర
భబ
yopt =0.010g/min.
As per the optimal level again the experiment is performed as A4, B2, C2 and D4.
The Experimental value that is obtained is 0.009g/min. So the value of percentage change is
10%.
4. CONCLUSION
In the present study, for EDM process the effect of current, pulse-on time, pulse off
time and voltage has been investigated. The effect of input parameter on output response
Material removal rate was analyzed for work material tungsten carbide (K-10). L16
orthogonal array based on Taguchi design and ANOVA was performed for analyzing the
result.
For the MRR, voltage is most influencing factor and then discharge current and after
that pulse-on time and last is pulse off time.MRR increases with the increase in the value of
voltage. After that current also affect the MRR with increases. Other factors for higher value
of pulse on time and pulse off the MRR are highest.
REFERENCES
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[2] Soo Hiong Lee, Xiaoping Li. 2003. Study of the Surface Integrity of the Machined Workpiece
in the EDM of Tungsten Carbide” Journal of Materials Processing Technology 139, pp. 315-321.
[3] Singh, S., Maheshwari, S., Pandey, P.C., 2004. Some Investigation into the Electric Discharge
Machining of Hardened Tool Steel Using Different Electrode Materials. Journal of Materials
Processing Technology 149 (1-3), pp. 272-277.
[4] George P.M., Raghunath B.K., Manocha L.M., M.W. Ashish., 2004. EDM Machining of
Carbon-Carbon Composite-a Taguchi Approach. Journal of Materials Processing Technology
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[5] Lee S.H., Li X.P., 2001. Study of the Effect of Machining Parameters on the Machining
Characteristics in Electrical Discharge Machining of Tungsten Carbide. Journal of Materials
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[6] N.Vijayponraj, Dr.G.Kalivarathan and Vettivel.S.C, “Investigation Of Mechanical Behaviour
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