1. selection of machining parameters/conditions. The important input

1. Abstract

In this
investigation, the effect of four controllable input variables of EDM namely: peak
current, pulse on time, pulse off time and gap voltage on surface roughness
(Ra), is investigated. A Box-Behnken design matrix is used to conduct experiments
on Mild Steel using Copper electrode. RSM is used on experimental data to model
the response. Analysis of variance at 95% confidence interval is performed and
significant coefficients are obtained. From the ANOVA results it is observed
that peak current and pulse on time are most significant factors. Optimization
of input parameters is done to obtain minimum Surface Roughness (SR).

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

2. Introduction

2.1 Principle of EDM

Electrical
Discharge Machining is a non-traditional machining process in which the metal
is removing from the work piece due to erosion case by rapidly recurring spark
discharge taking place between the tool and work piece. There are three phases
occurred in electrical discharge machining process. In the initial phase the
ignition breaks down the high voltage to low. Then the peak current increases
the energy and the material is removed from the workpiece. In the last phase,
plasma channel collapses and flushing flushes away the removed particles. In
EDM the component produced is the exact replica of the electrode. Through EDM complex
shaped products are manufactured that cannot be produced by conventional
method. In this process both the workpiece and the tool have no physical
contact with each other. Both are immersed in the dielectric liquid which also
act as coolant.

2.2 Machining Parameters of
EDM

An
important consideration in Electrical Discharge machining (EDM), like other machining
operations, is the selection of machining parameters/conditions. The important input
parameters that are affecting performance parameters in EDM are (1) Pulse ON
time (2) Pulse OFF time (3) Arc gap (4) Discharge current (5) Voltage (6) Polarity
(7) Duty cycle (8) Diameter of electrode (9) Dielectric fluid

2.3 Performance Parameters of
EDM

The performance
of the electrical Discharge Machining is measured by the parameters as Overcut
(OC), Material Removal Rate (MRR), Tool Wear Rate (TWR) and Surface Roughness (SR).

3. Literature Survey

Balasubramanian and Senthilvelan 1 conducted the experiments on EN8
and D3 steel materials using Cast Copper and Sintered Powder Metallurgy Copper
(P/M Copper) electrodes on EDM. He used the RSM to analyze and optimize the
input parameters. Singh and Singh 2 analysed the effect of different
materials on surface roughness. The result shows that the surface roughness
increases with increasing pulsed current and pulse time. Santoki and Ashwin 3 reviewed the development done in
the EDM & the effects of machining parameters on performance parameters
with various DOE & Optimization Techniques. Kumar, Sivakumar 4 conducted the experiments on AISI
D3 steel using Taguchi’s L18 OA on EDM. The effect of input parameters
including pulse on time, pulse off time, current and voltage on the material
removal rate (MRR) and surface roughness (Ra) was analysed and then optimized. Bhaumik and Maity 5 performed the electrode discharge machining
of AISI 304 stainless steel by using the tungsten carbide electrode in order to
analyse the effect of peak current, pulse on time, gap voltage, duty cycle on surface
roughness (Ra). The effect of significant process parameters on the response
has been studied. Then regression analysis is done and mathematical model is
created to describe the correlation of parameters. The result shows that the
most influential parameter for surface roughness is peak current. Pradhan and Biswas 6 designed a face centred central
composite design matrix and used it to conduct the experiments on AISI D2 tool
steel with copper electrode. RSM is used on experimental data to make the
regression model. It is found that discharge current and pulse duration are
significant factors. Torres, Luis 7 studied the behaviour of input
parameters of current intensity supplied by the generator (I), duty cycle (?),
pulse time (ti), and polarity on INCONEL 600 alloy using electrical discharge
machining (EDM). The experimental results confirm that positive polarity leads
to higher MRR whereas negative polarity leads to lower Ra values. Khan, Rahman 8 studied the surface finish
characteristics of the machined surface in EDM on Ti-5Al-2.5Sn titanium alloy.
central composite design is used to analyze the effects of peak current,
pulse-on time, pulse-off time, servo voltage and electrode material. The result
shows that surface roughness (SR) increases with peak current and pulse-on time
and decreases with servo voltage. Besides, the effect of the process parameters
on surface roughness depends on electrode material.

4. Experimental Details

4.1 Procedure

In this work, experiments
are performed by electrical discharge machining on the mild steel workpiece by
copper electrode in different machining conditions. Experiments are designed in
order to investigate the effect of different input EDM parameters namely
discharge current, pulse ON time, pulse OFF time and gap voltage on the EDM
output parameter of the interest namely surface roughness Ra.

4.2 Machine Tool

A Neu-ar M50
die sinking EDM machine is used. It has Heidenhain EDM controller and 9 sets
coordinate memory. It contains Built-in origin and mold centre setting
function. It has the lowest machining depth display function. And electrode
consumption offset function. Also a built-in residue proofing feature to drive
out carbon residue is present. It is equipped with high precision Heidenhain 1?m
linear encoder. It has durable wear-resistant Teflon Slippery track in “V
shape” and “horizontal track”. It also contains multiple fire- proof detection
system.

Figure 1- Electrical Discharge Machine Model M50

4.3 Material

4.3.1 Workpiece

A square plate
of Mild steel having dimensions 20*12*0.5cm was taken. This has 14.30 g/cm3 of
density, 1240 HV10 hardness, 2597°C of melting point and 420 kgf/mm2 of
compressive strength.

4.3.2 Electrode 

A Copper
electrode of cylindrical shape with 10 mm diameter and 100 mm length under
negative polarity was axially mounted within mild steel workpiece. The
properties of Cu electrode used in this work are the following: melting point
of 3500°C and density of 12.6 g/cm3.

4.4 DOE

In order to
reduce the number of experiments due to limited resources, Box-Behnken design
is used. Four factors each at three levels are taken. One block is made having
twenty-four factorial points and six centre points, so the total number of
experiments are 30. Design-Expert 7.0, 2005 is used to make the randomize
design. Machining was carried out to remove approximately 0.5mm from the top
surface. The different levels of factor considered for this study are
illustrated in Table 1.

Table 1 – Factors and Levels

S.No

Input
parameters

Level

Unit

-1

0

1

1

Peak
Current

1

5

9

Amp

2

Pulse
ON time

50

75

100

Microsec

3

Pulse
OFF time

80

100

120

Microsec

4

Gap
Voltage

40

50

60

Volts

 

4.5 Measurement of Response

Ra is a measure
of the surface finish quality of a product. It is defined as the arithmetic
value of the profile from the centreline along the length.

After
performing the machining operation, the surface roughness of each cut is
measured using a portable stylus type profilometer, Roughness measurement is
done in the traverse direction on the workpiece and the values of Ra parameter
are recorded.

Table 2 – Design Matrix

Run

Ip

Ton

Toff

V

Ra

1

1

50

120

40

2.39

2

1

50

80

40

2.15

3

9

100

120

60

7.08

4

9

100

80

60

7.64

5

9

100

80

40

7.43

6

5

75

100

50

5.41

7

9

50

80

40

6.23

8

1

100

120

40

2.09

9

1

100

80

40

1.65

10

1

100

80

60

1.74

11

9

100

120

40

8.66

12

9

50

120

40

6.01

13

5

75

100

50

5.22

14

1

50

80

60

2.11

15

9

50

120

60

6.24

16

1

100

120

60

2.15

17

5

75

100

50

5.29

18

1

50

120

60

2.45

19

5

75

100

50

5.36

20

9

50

80

60

5.83

21

9

75

100

50

6.48

22

5

75

100

50

5.6

23

5

100

100

50

5.81

24

5

75

100

50

5.53

25

1

75

100

50

1.98

26

5

75

80

50

5.54

27

5

75

100

40

5.97

28

5

75

100

60

5.52

29

5

50

100

50

4.77

30

5

75

120

50

5.77

 

5. Result and Discussion

5.1
ANOVA

Experiments are conducted to analyze the effect of machining
parameters on surface roughness. Design Expert Software was used to find out
the relationship between the input factors and the response Ra.

To decide the degree of the regression model, the R2 and
R2 adjusted values are summarized in Table 3 for various models. The
table shows that quadratic model is best with R2 = 99% Therefore,
the quadratic model is considered for regression analysis.

Table 3 – R2 and R2 adj test for surface roughness regression
model

Source

Std. Dev.

R-Squared

Adjusted R-Squared

Linear

0.73

0.89

0.87

2FI

0.69

0.92

0.88

Quadratic

0.28

0.99

0.98

 

Table 4 – Result of the ANOVA table for surface roughness
(Before elimination)

Source

Sum of Square

Df

Mean Square

F Value

p-value

Model

117.06

14.00

8.36

103.65

0.0001

A-Peak Current

102.20

1.00

102.20

1266.89

0.0001

B-Ton

2.05

1.00

2.05

25.37

0.0001

C-Toff

0.35

1.00

0.35

4.37

0.0539

D-Gap Voltage

0.18

1.00

0.18

2.28

0.1517

AB

3.97

1.00

3.97

49.21

0.0001

AC

0.02

1.00

0.02

0.25

0.6231

AD

0.18

1.00

0.18

2.27

0.1530

BC

0.04

1.00

0.04

0.44

0.5192

BD

0.07

1.00

0.07

0.89

0.3612

CD

0.07

1.00

0.07

0.92

0.3526

A^2

3.95

1.00

3.95

48.96

0.0001

B^2

0.08

1.00

0.08

0.98

0.3380

C^2

0.09

1.00

0.09

1.16

0.2977

D^2

0.20

1.00

0.20

2.52

0.1330

Residual

1.21

15.00

0.08

Lack of Fit

1.11

10.00

0.11

5.37

0.0387

Pure Error

0.10

5.00

0.02

Cor Total

118.27

29.00

 

Table 4 is an ANOVA summary which shows the F and P values for
different terms. The results show that in main effects ‘Voltage’ is
insignificant. Also all the quadratic terms except A2 and AB, are
insignificant. Thus, these terms are eliminated for the further analysis.

After elimination of insignificant terms, ANOVA is performed. The
result of ANOVA is summarized in Table 5. After elimination of non-significant
terms, the values of R2 and R2adj are 98.1% and 97.8%, respectively. The main
and interaction effects, that are significant, are Ip, Ton, Toff, Ip2, and
Ip×Ton.

Table 5 – The ANOVA table for the fitted model

Source

Sum of Square

Df

Mean Square

F Value

p-value

Model

116.07

5.00

23.21

253.78

0.0001

A-Peak Current

102.20

1.00

102.20

1117.25

0.0001

B-Ton

2.05

1.00

2.05

22.38

0.0001

C- Toff

0.35

1.00

0.35

3.86

0.0612

AB

3.97

1.00

3.97

43.40

0.0001

A^2

7.50

1.00

7.50

82.03

0.0001

Residual

2.20

24.00

0.09

Lack of Fit

2.09

19.00

0.11

5.34

0.0361

Pure Error

0.10

5.00

0.02

Cor Total

118.27

29.00

 

From this analysis, the simplest model obtained is stated in the
following equation.

Ra = 1.605 + 0.8601*Ip – 0.0114*Ton +0.007*Toff – 0.063Ip2
+0.005*Ip*Ton

Normal probability plot of the residuals is displayed in Fig. 2. It
can be seen that the residuals are almost falling on a straight line, which
indicates that the errors are normally distributed.

Figure 2
– Normal probability plot of residuals

Figure 3 – Predicted vs. experimental surface roughness

Fig. 3 depicts the comparison of experimental observations verse
the predicted response values. It can be examined that the regression model
likely fits the experimental values.

5.2 Influence of Input Parameter
On Response

Figure 4 – Effect of factors on Ra

Fig. 4 depicts
the plots of main effects on Ra. The plot shows that peak current is the most
influential factor. As the peak current increases the surface roughness increases
rapidly. Also with the increase in Ton, Ra also increases. Trend for Toff is
same i.e. Ra increases with the increase in Toff.

5.3 Model Graph

Fig. 5 represents contour plot and response surface for Surface
Roughness in relation to input parameters of peak current and pulse ON time. It
can be concluded that the at any value of Ton, the Ra increases rapidly with
the increase in Ip. Hence, in order to obtain minimum Ra, peak current should
be at low level (1A) and pulse on time on (50?s).

Fig. 6, depicts the contour plot and response surface for Ra in
relation to Ip and Toff, where Ton remains constant at the level of 75?s. It
can be seen that, when Ip increases Ra also increases. However, Ra drops slowly
decreases with the increase in Toff at lower Ip, and at higher Ip Ra increases
with Toff. However, the influence of Toff on Ra is very low as compared to Ip
and Ton.

Figure 5 – Contour & Response surface plot depicting the
effect of Ip and Ton on Ra

Figure 6 – Contour & Response surface plot depicting the
effect of Ip and Toff on Ra

Figure 7 – Contour & Response surface plot depicting the
effect of Ton and Toff on Ra

             

Finally, Fig.7 depicts the contour plot and response surface for Ra
in relation to Ton and Toff, where Ip remains constant at the level of 5 A.
From these plots, it can be concluded for the given range of experiments
conducted for this test, that peak current and pulse ON time are directly
proportional to the Ra and for pulse OFF time the effect is very less as
compared to the other parameters.

6. OPTIMIZATION

EDM is a useful
and valuable tool tom make complex shape parts that cannot be machined by
traditional machining processes. In order to increase the quality and rate of
production, the process parameters have to be optimised. Also, especially in
case of EDM, it is very essential to optimize the input parameters to yield
minimum SR. In single objective optimization only one solution has been
obtained. it has been observed that low Peak current, low Pulse on time, low
pulse off time and marginal Voltage gives minimum Surface roughness.

Table 6 – Optimization Table

S. No

Input /output Parameters

Optimized value

Units

1

Peak current

1

Amps

2

Pulse on time

100

Microsec

3

Pulse off time

80

Kg/Cm2

4

Voltage

56

V

5

Surface Roughness

1.77801

?m

 

7. Conclusion

In this study
the effect of most significant input parameters of EDM on the surface roughness
has been studied for Mild steel. A Box-Behnken design with factors of discharge
current, pulse on time pulse off time and gape voltage, is used for
experimentation. The ranges of these parameters are chose from the literature
review. Using the Response Surface Methodology (RSM), the regression mode
(quadratic) is formed using the Design-Expert 7.0 software.

The analysis
shows that the output response is significantly affected by the input
parameters of discharge current, pulse on time, pulse off time and gap voltage
with 95% confidence interval. The results also reveal that the value of
discharge current, pulse on time and pulse off time should be set as low as
possible, in order to get a good surface finish on mild steel. For the best
setting of Ra, the discharge current of 1 A, pulse on time of 100?s and off
time should be 50?s, which yields the best value Ra of 1.778?m. The regression
model developed for surface roughness can be effectively used for the optimal
selection of input parameters in EDM to achieve good surface finish for Mild
steel workpiece. These findings will be helpful to manufacturing engineers in
selecting the appropriate parametric combinations for EDM processes to
accomplish desired levels of Ra.

References

1.         Balasubramanian, P. and T.
Senthilvelan, Optimization of machining parameters in EDM process using cast
and sintered copper electrodes. Procedia Materials Science, 2014. 6: p. 1292-1302.

2.         Singh, H. and E. Singh, Examination of
Surface Roughness Using Different Machining Parameter in EDM. 2012.

3.         Santoki, P.N. and P. Ashwin, A
review–status of recent developments and effect of machining parameters on
performance parameters in EDM. Int. J. Innov. Emerg. Res. Eng, 2015. 2(1): p. 32-41.

4.         Kumar, M.S., et al., ‘Parameters
Optimisation of Wire Electrical Discharge Machining on AISI D3 Steel with
Different Thickness’. International Journal of Applied Engineering Research,
2015. 10(62): p. 2015.

5.         Bhaumik, M. and K. Maity, Effect of
machining parameter on the surface roughness of AISI 304 in silicon carbide
powder mixed EDM. Decision Science Letters, 2017. 6(3): p. 261-268.

6.         Pradhan, M. and C. Biswas, Effect of
process parameters on surface roughness in EDM of tool steel by response
surface methodology. International Journal of Precision Technology, 2011. 2(1): p. 64-80.

7.         Torres, A., C. Luis, and I. Puertas,
Analysis of the influence of EDM parameters on surface finish, material removal
rate, and electrode wear of an INCONEL 600 alloy. The International Journal of
Advanced Manufacturing Technology, 2015. 80(1-4):
p. 123-140.

8.         Khan, M.A.R., M. Rahman, and K.
Kadirgama, An experimental investigation on surface finish in die-sinking EDM
of Ti-5Al-2.5 Sn. The International Journal of Advanced Manufacturing
Technology, 2015. 77(9-12): p.
1727-1740.