parametric optimization of near dry electrical discharge machining process for aisi sae d-2 tool...

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –

6480(Print), ISSN 0976 – 6499(Online), Volume 6, Issue 1, January (2015), pp. 99-114 © IAEME

99

PARAMETRIC OPTIMIZATION OF NEAR DRY

ELECTRICAL DISCHARGE MACHINING PROCESS FOR

AISI SAE D-2 TOOL STEEL

Mane S.G.1, Hargude N.V.

2

1,2

Department of Mechanical Engineering,

PVPIT Budhgaon, Sangli 416416,Maharashtra, India.

ABSTRACT

Present dissertation work has attempted to optimize the various significant process

parameters for near dry EDM process by Taguchi method and design of experiments. The response

variables are material removal rate (MRR), the surface roughness (SR) and tool wear rate (TWR). A

low cost mist delivery system (MQL fluid dispenser) to supply the mist (liquid-gas mixture) at a

controlled rate has been developed to conduct the experiments and has served it’s purpose

exceptionally well during the experimentation. The AISI SAE D-2 tool steel has been used as a

work-piece material. The kerosene air mixture has been used as a dielectric medium. The various

process parameters selected for the study were discharge current, gap voltage, pulse on time, duty

factor, air pressure and electrode material. A standard L18 orthogonal array was selected for design

of experiments. The results obtained from the experimental runs were analyzed by using Minitab15

software. ANOVA for S/N ratios was done to find the most contributing process parameters

affecting the MRR, TWR and SR. The best parametric settings for each of the maximum MRR,

minimum TWR and minimum SR were determined with the help of ANOVA. The corresponding

values of the response parameters were also calculated using mathematical formulae and confirmed

by performing validation experimentation. From the present experimental study, it is observed that

MRR, TWR and SR in near dry EDM process are mainly affected by the discharge current and

electrode material. Copper-tungsten electrode material exhibited lower SR and low TWR than that of

the copper electrode but higher MRR was obtained with copper electrode.

Keywords: Near dry EDM, Design of experiments, Taguchi method, ANOVA, MRR, SR, TWR.

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1. INTRODUCTION

The metal working fluids (MWFs) are extensively used in conventional machining processes.

The economical, ecological and health impacts of metal working fluids (MWFs) can be reduced by

using minimum quantity lubrication referred to as near dry machining. In near dry machining

(NDM), an air-oil mixture called an aerosol is fed onto the machining zone [10]. This concept of

near dry machining can be well applied in EDM process, the process being referred to as near-dry

EDM process. Advantages of near-dry EDM were identified as a stable machining process at low

discharge energy input because the presence of liquid phase in the gas environment changes the

electric field, making discharge easier to initiate and thus creating a larger gap distance. In addition,

good machined surface integrity without debris reattachment that occurred in dry EDM was attained

since the liquid in the dielectric fluid enhances debris flushing. Other potential advantages of near-

dry EDM are a broad selection of gases and liquids and flexibility to adjust the concentration of the

liquid in gas. The dielectric properties can thus be tailored in near-dry EDM to meet various

machining needs, such as high MRR or fine surface finish. Also Near dry EDM shows advantages

over the dry EDM in higher material removal rate (MRR), sharp cutting edge, less debris deposition

and better surface finish. Compared to wet EDM, near dry EDM has higher material removal rate at

low discharge energy and generates a smaller gap distance [2]. A comparative study of wet, dry and

near dry EDM has been tabulated in Table 1. But the technical barrier in near-dry EDM lies in the

selection of proper dielectric medium and process parameters. From the review of literature it is seen

that experimental investigations have been carried out in order to study the effect of various input

parameters like discharge current, gap voltage, pulse on time, gas pressure, fluid flow rate, electrode

orientation and spindle speed on material removal rate, surface roughness and tool wear rate and to

improve the performance of near dry EDM process [1-6, 16].

Table1. Comparison of wet, dry and near dry EDM processes

Sr. No Aspect Wet EDM Dry EDM Near Dry EDM

1 Dielectric medium Liquids Gases Liquid-gas mixture

2 Dielectric used

Hydrocarbon based oils,

Kerosene, EDM oil,De-

ionized water

Air, Oxygen gas,

Argon gas, Nitrogen

gas, Helium gas etc

Water-air, Water-

oxygen, Kerosene-

air, Kerosene-

nitrogen mixtures etc.

3 Dielectric

consumption Heavy ---- Very less

4 Pollution problem Major problem Odor of burning Very less

5 Fire Hazard Highly flammable oils-

more fire hazard No fire hazard No fire hazard

6 Energy input High Low Low

7 Process stability Good Poor (arching

problem) Good

8 Debris reattachment No Major problem No

9 Discharge Initiation ----- Difficult Easier

10 Dielectric properties Can’t be tailored Can be tailored Can be tailored

11 Gap Distance more ----- Less

12 Surface finish Good poor Good

13 Surface Integrity Good poor Good

14 MRR Lower Higher Higher

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976

6480(Print), ISSN 0976 – 6499(Online), Volume 6, Issue 1, January (2015), pp.

However, irrespective of its inherent advantages over wet and dry EDM processes, not much

attention has been given towards the parametric optimization of the near

necessary to optimize the input parameters for maximum material remova

the surface roughness (SR) to make the near dry EDM process cost effective and economically

viable one. In the present study, the best parametric settings for each of the maximum MRR,

minimum TWR and minimum SR have been determine

2. EXPERIMENTAL SET UP

The experimentation was carried out on the Electronica make smart ZNC sinker EDM. A

mist delivery system (MQL fluid dispenser) developed was used to supply the mist (kerosene

mixture) at a controlled rate to the gap between work

smaller quantity of liquid was formed and a very sharp and fine spray of the mist

became possible to machine the components using very small fluid flow

idea of minimum quantity lubrication (MQL)

of dielectric fluid (kerosene) and

near-dry in a true sense. The exper

developed for experimentation. The responses selected for

rate (MRR), tool wear rate (TWR) and surface roughness (SR). Response characteristics are given i

the Table 2.

Fig.1. Experimental Setup for Near

Response name

Material Removal Rate

(MRR) Tool Wear Rate (TWR)

Surface Roughness (SR)

2.1. Selection of the process parameters and their levels

The process parameters and their levels given in

literature survey and considering the range limitation of EDM

levels for each of the parameters B, C, D, E and F are selected beca

Work piece

Electrode

Rotating tool Arrangement

anced Research in Engineering and Technology (IJARET), ISSN 0976

6499(Online), Volume 6, Issue 1, January (2015), pp. 99-114

101


attention has been given towards the parametric optimization of the near-dry EDM process. It is

necessary to optimize the input parameters for maximum material removal rate (MRR) and minimize



minimum TWR and minimum SR have been determined with the help of ANOVA


mist delivery system (MQL fluid dispenser) developed was used to supply the mist (kerosene

a controlled rate to the gap between work-piece & electrode. A perfect mist with a

formed and a very sharp and fine spray of the mist

became possible to machine the components using very small fluid flow rate of 4 ml/min. Hence the

idea of minimum quantity lubrication (MQL) could be implemented, consuming very small amount

of dielectric fluid (kerosene) and giving justice to the name of the process and making the process

The experimental setup shown in Figure 1 shows the mist delivery system

The responses selected for experimentation were material removal

rate (MRR), tool wear rate (TWR) and surface roughness (SR). Response characteristics are given i

Experimental Setup for Near- Dry EDM and sparking achieved

Table 2. Response Characteristics

Response type Unit

Material Removal Rate Larger the better, gm/min

Tool Wear Rate (TWR) Smaller the better gm/min

Surface Roughness (SR) Smaller the better Ra value in

microns

Selection of the process parameters and their levels

The process parameters and their levels given in Table 3 were selected based on extensive

literature survey and considering the range limitation of EDM machine [12, 14, 15, and 17]

levels for each of the parameters B, C, D, E and F are selected because the non

Spray gun

anced Research in Engineering and Technology (IJARET), ISSN 0976 –

14 © IAEME


dry EDM process. It is

l rate (MRR) and minimize



d with the help of ANOVA and S/N ratios.


mist delivery system (MQL fluid dispenser) developed was used to supply the mist (kerosene-air

. A perfect mist with a

formed and a very sharp and fine spray of the mist was achieved and it

rate of 4 ml/min. Hence the

, consuming very small amount

to the name of the process and making the process

shows the mist delivery system

experimentation were material removal

rate (MRR), tool wear rate (TWR) and surface roughness (SR). Response characteristics are given in

and sparking achieved

gm/min

gm/min

Ra value in

microns

able 3 were selected based on extensive

machine [12, 14, 15, and 17]. Three

use the non-linear behavior of

Sparking



102

process parameters can only be studied if more than two levels of a parameter are used [18]. Also

some of the constant parameters and their values or conditions selected for the experimentation are

tabulated in Table 4[12, 14, 15, and 17].

Table 3. Process parameters under study and their levels

Factors Levels

Level 1 Level 2 Level 3

Electrode material (A) Copper-Tungsten Copper ------

Air pressure (B) kg/cm2

4 5 6

Discharge Current (C) Amps 8 12 16

Gap voltage (D) volts 40 60 80

Pulse on time (E) µs 100 150 200

Duty factor (F) % 7 9 11

Table 4. Constant parameters and their values / conditions for experimentation

2.2 Selection of the orthogonal array

In the present experiment, the L18 orthogonal array meets the requirements of experiment as it

is a smallest mixed 2-level and 3-level array [18]. The experimentation was carried out as per the L18

orthogonal array given in Table 5.

Table 5 Design of Experiments L18 (213

5) array

Expt.

No.

Electrode

material

Air pressure

kg/cm2 Discharge

current Amps

Gap voltage

volts Pulse on time Duty Factor

01 Cu W 4 8 40 100 7

02 Cu W 4 12 60 150 9

03 Cu W 4 16 80 200 11

04 Cu W 5 8 40 150 9

05 Cu W 5 12 60 200 11

06 Cu W 5 16 80 100 7

07 Cu W 6 8 60 100 11

08 Cu W 6 12 80 150 7

09 Cu W 6 16 40 200 9

10 Cu 4 8 80 200 9

11 Cu 4 12 40 100 11

12 Cu 4 16 60 150 7

13 Cu 5 8 60 200 7

14 Cu 5 12 80 100 9

15 Cu 5 16 40 150 11

16 Cu 6 8 80 150 11

17 Cu 6 12 40 200 7

18 Cu 6 16 60 100 9

Parameters Value/Condition

Work-piece material AISI SAE D2 tool steel

Work-piece size 50 mm * 50 mm * 6 mm

Tool electrode diameter 15 mm

Tool electrode rotating/stationary Rotating

Dielectric medium Kerosene-Air mixture

Fluid flow rate 4 ml/min

Polarity Straight (Electrode –ve, work-piece +ve)

Machining time 20 min.



103

2.3 Experimental procedure

Copper-Tungsten and copper as the tool electrode materials and kerosene-air mixture as the

dielectric medium were used for conducting the experiments [12]. A constant fluid flow rate of 4

ml/min for kerosene was maintained throughout the experimentation. The straight polarity (Electrode

–ve, work-piece +ve) was maintained during the experimentation [2]. A rotating tool arrangement

was used to keep the electrode rotating at a constant speed during machining [12]. The various

process parameters and their levels shown in table 5 were set while conducting each of the

experimental run. Total eighteen no. of experimental runs each of 20 min duration were carried out

as per the design matrix.

3. MEASUREMENT OF RESPONSE PARAMETERS

3.1 Measurement of MRR

MRR of each sample is calculated from weight difference of work piece before and after the

performance trial, which is given by:

�� = (��)� � / �� (Equation ....1)

Where Wi = Initial weight of work piece material (gm)

Wf = Final weight of work piece material (gm)

t = Time period of trail in minutes

The weights of the work-pieces before and after machining for calculation of MRR were measured

using a weighing machine of Contech model CA-503.

3.2 Measurement of TWR TWR of each sample is calculated from weight difference of tool electrode before and after

the performance trial, which is given by:

�� = (��)� � / �� (Equation ....2)

Where Ti = Initial weight of tool electrode (gm)

Tf = Final weight of tool electrode (gm)

t = Time period of trail in minutes

The weights of the electrodes before and after machining for calculation of TWR were measured

using a weighing machine of Contech model CA-503.

3.3 Measurement of SR

Surface roughness was measured using the Surf Test model SJ210 of Mitutoyo, Japan.

Surface roughness of each sample was measured at three different locations of machined area and a

mean is taken.

3.4 Experimental Results and S/N ratios

The experimental results for material removal rate, tool wear rate and surface roughness by

varying the selected control parameters as per L18 orthogonal array are shown in Table 6. The S/N

ratios worked out by using MINITAB 15 software are also tabulated in Table 6.



104

Table 6. Results for MRR, TWR and SR

Expt.

No. MRR gm/min TWR gm/min

SR

Ra

SN Ratio of

MRR

SN Ratio of

TWR

SN Ratio of

SR

01 0.00915 0.0004000 3.220 -40.7716 67.9588 -10.1571

02 0.01465 0.0005400 3.314 -36.6832 65.3521 -10.4071

03 0.06196 0.0006500 4.447 -24.1578 63.7417 -12.9613

04 0.00980 0.0004200 2.908 -40.1755 67.5350 -9.2719

05 0.01339 0.0006200 2.770 -37.4644 64.1522 -8.8496

06 0.09980 0.0007000 3.934 -20.0174 63.0980 -11.8967

07 0.00640 0.0003000 3.179 -43.8764 70.4576 -10.0458

08 0.08590 0.0008225 3.909 -21.3201 61.6973 -11.8413

09 0.07820 0.0006350 4.138 -22.1359 63.9445 -12.3358

10 0.05730 0.0008000 4.420 -24.8369 61.9382 -12.9084

11 0.01280 0.0007500 3.948 -37.8558 62.4988 -11.9275

12 0.07765 0.0011500 4.928 -22.1972 58.7860 -13.8534

13 0.08210 0.0008000 4.419 -21.7131 61.9382 -12.9065

14 0.06750 0.0018000 3.447 -23.4139 54.8945 -10.7488

15 0.09940 0.0013500 3.818 -20.0523 57.3933 -11.6367

16 0.06060 0.0011000 4.527 -24.3505 59.1721 -13.1162

17 0.08085 0.0015500 3.722 -21.8464 56.1934 -11.4155

18 0.07605 0.0005500 4.922 -22.3780 65.1927 -13.8428

4. RESULTS AND DISCUSSION

All observations are transformed into S/N ratio and results for S/N ratios of have been

analyzed by ANOVA method to find the significance of various control parameters and their best

level. The analysis and graphical presentations have been made using MINITAB 15 software. The

most significant parameters affecting the selected response variable and their best level value are

determined. The optimal design for each of the response parameter has been decided and confirmed

by conducting a confirmation test.

4.1 Analysis of Variance (ANOVA) for S/N ratios of MRR

The S/N ratio consolidates several repetitions into one value and is an indication of the

amount of variation present. The S/N ratios have been calculated to identify the major contributing

factors that cause variation in the MRR. MRR is “Larger is better” type response which is given by:

(S/N) LB = - 10 log (MSD) LB (Equation ….3)

Where(��)�� = ��∑ �!"#$��%� (Equation …..4)

(MSD)LB = Mean Square Deviation for Larger-the-better response.

where, ‘y’ is value of response variable and ‘n’ is number of observations in the experiments.

Table 7 shows the ANOVA results for S/N ratio of MRR at 99 % confidence interval.

Discharge current was observed to be the most significant factor affecting the MRR, followed by

electrode material and gap voltage according to F test. All the remaining parameters namely, pulse

on time, duty factor and air pressure are insignificant to affect the MRR.



The percentage contribution of each of the control parameter can be calculated by the

following formula:

% contribution of control factor= [SS (Respective Factor)/SS (Total)] *100

For example, for discharge current,

% contribution = [375.61/1233.12]* 100 = 30.46 %

Table 7.Analysis of Variance for S/N ratios

Source DF Seq SS

Electrode

material 1 256.57

Air pressure 2 85.79

Discharge

current 2 375.61

Gap voltage 2 229.99

Pulse on Time 2 112.26

Duty Factor 2 132.97

Residual Error 6 39.93

Total 17 1233.12

S = 2.580 R

S: Significant factor; NS: Non

Fig. 2.Percentage contribution of control parameters for MRR

The percentage contribution of each of the control parameters under study for MRR is shown by

a pie chart in Figure 2. It can be seen that discharge current contributes

followed by electrode material (20.80 %) and gap voltage (18.66 %).

used to calculate mean of S/N ratios at three levels of all factors and are given in Table

rank of all factors in this study considering the mean of S/N ratios for MRR at different levels in

terms their relative significance. Current has the highest rank signifying highest contribution to

Gap voltage

Duty Factor

10.78

Pulse on Time

9.11

Air pressure

Percentage contribution of control parameters for MRR



105



(Equation ….5)

For example, for discharge current,

% contribution = [375.61/1233.12]* 100 = 30.46 %

Analysis of Variance for S/N ratios of MRR

Adj SS Adj MS F P Contribution

256.57 256.572 38.5

5

0.00

1 85.79 42.593 6.45 0.03

2 375.61 187.860 28.2

2

0.00

1 229.99 114.994 17.2

8

0.00

3 112.26 56.130 8.43 0.01

8 132.97 66.487 9.99 0.01

2 39.93 6.695

100.00

S = 2.580 R-Sq = 96.8% R-Sq(adj) = 90.8%

S: Significant factor; NS: Non- significant factor



igure 2. It can be seen that discharge current contributes significantly (30.46 %),

followed by electrode material (20.80 %) and gap voltage (18.66 %). S/N ratio values of MRR are

used to calculate mean of S/N ratios at three levels of all factors and are given in Table

tudy considering the mean of S/N ratios for MRR at different levels in


Discharge current

30.46

Electrode material

20.8Gap voltage

18.66

Air pressure

6.95Error

3.24


Discharge current

Electrode material

Gap voltage

Duty Factor

Pulse on Time

Air pressure

Error


14 © IAEME



(Equation ….5)

Contribution

% Remark

20.81 S

06.96 NS

30.46 S

18.65 S

09.10 NS

10.78 NS

03.24

100.00

Sq(adj) = 90.8%



significantly (30.46 %),

S/N ratio values of MRR are

used to calculate mean of S/N ratios at three levels of all factors and are given in Table 8. It gives us

tudy considering the mean of S/N ratios for MRR at different levels in


Discharge current

Electrode material

Gap voltage

Duty Factor

Pulse on Time

Air pressure

Error



106

MRR, followed gap voltage and electrode material. Air pressure has the lowest rank. Duty factor and

pulse on time were observed to be insignificant in affecting MRR.

Table 8.Response Table for Signal to Noise Ratios of MRR

Level Electrode

material Air Pr.

Discharge

current Gap voltage

Pulse on

time Duty factor

1 -31.84 -31.08 -32.62 -30.47 -31.39 -24.64

2 -24.29 -27.14 -29.76 -30.72 -27.46 -28.27

3 -25.98 -21.86 -23.02 -25.36 -31.29

Delta 7.55 5.10 10.80 7.70 6.03 6.65

Rank 3 6 1 2 5 4

cucuw

-21

-24

-27

-30

-33

654 16128

806040

-21

-24

-27

-30

-33

200150100 1197

Electrode mtl.

Mean of SN ratios

air pr. dis. cu

gap vol pulse on time duty factor

Main Effects Plot for SN ratios

Data Means

Signal-to-noise: Larger is better

Fig. 3.Main effects plot for S/N ratios of MRR

Main effects plot for S/N ratios of MRR is shown in the Figure 3. The graph shows that with

increase in discharge current, S/N ratio increases. The S/N ratio increases with an increase in pulse

on time and air pressure as well.

As can be observed from the graph, S/N ratio decreases slightly with an increase in gap voltage from

40 V to 60 V. However, a steep increase in S/N ratio can be observed from a gap voltage of 60V to

80V. Further it can be observed that S/N ratio reduces with an increase in duty factor.

Lastly, it can be observed that out of the two electrode materials, Copper electrode has

larger S/N ratio compared to Copper-Tungsten electrode.

For optimizing a product or process design, S/N ratio is used because additivity of factor

effects is good when an appropriate S/N ratio is used. Otherwise, large interactions among the

control factors may occur resulting in high cost of experimentation and potentially unreliable results.

In optimization, we use S/N ratio as the objective function to be maximized[18]. To conclude the

discussion, for maximum MRR, the level value with higher S/N ratio of each of the control

parameter under study should be selected at this stage. Thus, with high discharge current of 16A,

high pulse on time of 200 µs, high gap voltage of 80 V, low duty factor of 7, higher air pressure of 6

kg/cm2 and copper electrode should be selected.

Thus, it can be concluded that the optimum combination for MRR is A2 B3 C3 D3 E3 F1.



107

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, which is not available in L18 array under study. Hence theoretical optimum value of

MRR has to be calculated.

The estimated S/N ratio using the optimal level of the design parameters can be calculated. The

optimal value of S/N ratio is given by the formula

nopt=nm+∑ai=1 (ni- nm) (Equation …..6)

where nm is the total mean S/N ratio, ni is the mean S/N ratio at optimum level and’ a’ is the number

of main design parameters that effect quality characteristic. Based on the above equation the

estimated multi-response signal to noise ratio can be obtained.

nopt = -28.0692+(-24.29+28.0692) + (-25.98+28.0692) + (-21.86+28.0692)

+ (-23.02+28.0692) + (-25.36+28.0692) + (-24.64+28.0692)

nopt = Optimal value of S/N ratio = -4.804

The corresponding value of MRR is given by the formula

&' = ��(

)ƞ*+,-.

(Equation …..7)

Thus, y2 = 0.3308

y opt = 0.5751 gm/min

A confirmation experiment is performed by setting the control parameters as per the optimum

levels achieved. The experimental result obtained for the MRR is 0.5628 gm /min. Thus, the

experimental value agrees reasonably well with prediction. The maximum deviation of predicted

result from experimental result is about 2.14 %. Hence, the experimental result confirms the

optimization of MRR using Taguchi method and the resulting model seems to be capable of

predicting MRR.

4.2 Analysis of Variance (ANOVA) for S/N ratios of TWR

The S/N ratios have been calculated to identify the major contributing factors that cause

variation in the TWR. TWR is “Smaller is better” type response which is given by:

(S/N) SB = - 10 log (MSD) SB (Equation …...8)

Where(��)�� = ��∑ (&�')��%� (Equation..…..9)

(MSD)SB = Mean Square Deviation for smaller-the-better response.

where, ‘y’ is value of response variable and ‘n’ is number of observations in the experiments.

Table 9 shows the ANOVA results for S/N ratio of TWR at 94 % confidence interval.

Electrode material was observed to be the most significant factor affecting the TWR, followed by

discharge current and gap voltage according to F test. All the remaining parameters namely, pulse on

time, duty factor and air pressure are insignificant to affect the TWR.

The percentage contribution of each of the control parameters under study for TWR is shown

by a pie chart in Figure 4. It can be seen that electrode material contributes significantly (47.7%),

followed by discharge current (17.68 %) and gap voltage (13.07%).

S/N ratio values of TWR are used to calculate mean of S/N ratios at three levels of all factors

and are given in Table 10. It gives us rank of all factors in this study considering the mean of S/N

ratios for TWR at different levels in terms their relative significance.



Table 9. Analysis of Variance for S/N ratios of TWR

Source DF Seq SS

Electrode 1 138.500

Air pr. 2 11.025

Discharge 2 51.344


Pulse on 2 19.621

Duty Factor 2 8.134


Total 17 290.363

S = 1.991


Fig. 4. Percentage contribution of control parameters for TWR

Table 10

Level Electrode

material

1 65.33 63.38

2 59.78 61.50

3 62.78

Delta 5.55 1.88

Rank 1 5

Electrode material has the highest rank signifying highest contribution to TWR, followed by

discharge current and gap voltage. Duty factor has the lowest rank. Pulse on time and air pressure

were observed to be insignificant in affecting TWR.

Main effects plot for S/N ratios of TWR is shown in the

increase in discharge current from 8A to 12A, S/N ratio decreases. However as the discharge current

increases from 12A to 16A, S/N ratio go on increasing. The S/N ratio decreases with an increase in

pulse on time from 100 πs to 150

initially from 4 kg/cm2 to 5 kg/cm2 air pressure and shows an increasing trend as the air pressure

Discharge current

17.68

Gap voltage

13.07

Error

8.19

Pulse on Time

6.76

Percentage contribution of control parameters for TWR



108

Analysis of Variance for S/N ratios of TWR

Adj SS Adj

MS F P

Contribution

138.500 138.500 34.93 0.001 47.70

11.025 5.512 1.39 0.319 03.80

51.344 25.672 6.47 0.032 17.68

37.950 18.975 4.79 0.057 13.07

19.621 9.810 2.47 0.165 06.76

8.134 4.067 1.03 0.414 02.80

23.789 3.965 08.19

100.00

S = 1.991 R-Sq = 91.8% R-Sq(adj) = 76.8%



Table 10. Response Table for S/Noise Ratios of TWR

Air

Pr.

Discharge

current

Gap

voltage

Pulse on

time

63.38 64.83 62.59 64.02

61.50 60.80 64.31 61.66

62.78 62.03 60.76 61.98

1.88 4.04 3.56 2.36

5 2 3 4

material has the highest rank signifying highest contribution to TWR, followed by


were observed to be insignificant in affecting TWR.

ffects plot for S/N ratios of TWR is shown in the Figure 5. The graph shows that with



s to 150 πs. Further as the air pressure is increased S/N ratio decreases


Electrode material

47.7

Discharge current

Air

pressure

3.8

Duty

Factor

2.8


Electrode material

Discharge current

Gap voltage

Error

Pulse on Time

Air pressure

Duty Factor


14 © IAEME

Contribution

% Remark

S

NS

S

S

NS

NS

100.00

Sq(adj) = 76.8%


Response Table for S/Noise Ratios of TWR

Pulse on

time

Duty

factor

61.61

63.14

62.90

1.53

6

material has the highest rank signifying highest contribution to TWR, followed by


igure 5. The graph shows that with



s. Further as the air pressure is increased S/N ratio decreases



Electrode material

Discharge current

Gap voltage

Pulse on Time

Air pressure

Duty Factor



109

increases to 6 kg/cm2. It can be seen that, as the gap voltage increases from 40 V to 60 V, S/N ratio

increases and shows a decreasing trend further as the gap voltage increases to 80 V. Also as the duty

factor increases from 7 to 9, S/N ratio increases with a slight decrease thereafter as the duty factor

increases to 11. It can be also observed that, Copper-Tungsten electrode has larger S/N ratio

compared to Copper electrode.

CuCuW

66.0

64.5

63.0

61.5

60.0

654 16128

806040

66.0

64.5

63.0

61.5

60.0

200150100 1197

Electrode matl

Mean of SN ratios

Air Pr Disc. Currrent

Gap Voltage Pulse on Time Duty Factor


Data Means

Signal-to-noise: Smaller is better

Fig. 5 Main effects plot for S/N Ratios of TWR

To conclude the discussion, for minimum TWR, the level value with higher S/N ratio of each

of the control parameter under study should be selected at this stage. Thus, a low discharge current of

8A, low pulse on time of 100 µs, moderate gap voltage of 60 V, moderate duty factor of 9, low air

pressure of 4 kg/cm2 and copper-tungsten electrode material should be selected. Thus, it can be

concluded that the optimum combination for TWR is A1 B1 C1 D2 E1 F2. This optimal parametric

combination is not available in L18 array under study. Hence theoretical optimum value of TWR has

to be calculated.

By using the equation 6 from section 4.1, the estimated multi-response signal to noise ratio

can be obtained.

nopt = 62.5525+(65.33-62.5525) + (63.38-62.5525) + (64.83-62.5525) + (64.31-62.5525)

+ (64.02-62.5525) + (63.14-62.5525)

nopt = Optimal value of S/N ratio = 72.2475

The corresponding value of TWR is given by the formula

&' = 10)ƞ123

-. (Equation ……10)

Thus, y2 = 5.96005 * 10

-08

y opt = 0.000244 gm/min


levels achieved. The experimental result obtained for the TWR is 0.000255gm /min. Thus, the

experimental value agrees reasonably well with prediction. The maximum deviation of predicted

result from experimental result is about 4.51 %. Hence, the experimental result confirms the

optimization of TWR using Taguchi method and the resulting model seems to be capable of

predicting TWR.



4.3 Analysis of Variance (ANOVA) for S/N ratios of SR

The S/N ratios have been calculated to identify the major contributing

variation in the SR. SR is “Smaller is better” type response which is given by

section 4.2. Table 11 shows the ANOVA results for S/N ratio of SR at 93 % confidence interval.

Electrode material was observed to be the

discharge current and air pressure according to F test. All the remaining

time, duty factor and gap voltage are non

Table 11 Analysis of Variance

Source DF Seq SS

Electrode

material 1 11.8250

Air pr. 2 5.6070

Discharge

current 2 11.3788


Pulse on Time 2 0.6359

Duty Factor 2 1.1095


Total 17 38.0423

S = 0.7862 R


Fig. 6 Percentage contribution of control parameters for SR

The percentage contribution of each of the control parameters under study for SR is shown by

a pie chart in Figure 6. It can be seen that electrode material contributes significantly (31.08 %),

followed by discharge current (29.91 %) and air pressure (14.74 %)

S/N ratio values of SR are used to calculate mean of S/N ratios at three levels of all factors


ratios for SR at different levels in terms the

rank signifying highest contribution to SR, followed by Electrode material and air pressure. Pulse on

time has the lowest rank. Gap voltage and duty factor were observed to be insignificant in aff

SR. One thing to be noted here is that

Air pr.

14.74

Gap voltage

9.93

Residual Error

9.75

Duty Factor

Percentage contribution of control parameters for SR



110

4.3 Analysis of Variance (ANOVA) for S/N ratios of SR

The S/N ratios have been calculated to identify the major contributing

variation in the SR. SR is “Smaller is better” type response which is given by

Table 11 shows the ANOVA results for S/N ratio of SR at 93 % confidence interval.

Electrode material was observed to be the most significant factor affecting the SR, followed by

discharge current and air pressure according to F test. All the remaining parameters i.e. pulse on

time, duty factor and gap voltage are non-significant to affect the SR.

Table 11 Analysis of Variance for S/N ratios of SR

Adj SS Adj MS F P Contribution

%

11.8250 11.8250 19.13 0.005 31.08

5.6070 2.8035 4.54 0.063 14.74

11.3788 5.6894 9.20 0.015 29.91

3.7770 1.8885 3.05 0.122 9.93

0.6359 0.3179 0.51 0.622 1.67

1.1095 0.5548 0.90 0.456 2.92

3.7090 0.6182 9.75

100.00

S = 0.7862 R-Sq = 90.3% R-Sq(adj) = 72.4%



percentage contribution of each of the control parameters under study for SR is shown by


followed by discharge current (29.91 %) and air pressure (14.74 %).

are used to calculate mean of S/N ratios at three levels of all factors


R at different levels in terms their relative significance. Discharge current has the highest


time has the lowest rank. Gap voltage and duty factor were observed to be insignificant in aff

SR. One thing to be noted here is that there is very minute difference as far as the contribution of

Electrode material

31.08

Discharge current

29.91

Duty Factor

2.92

Pulse on Time

1.67

Percentage contribution of control parameters for SR

Electrode material

Discharge current

Air pr.

Gap voltage

Residual Error

Duty Factor

Pulse on Time


14 © IAEME

The S/N ratios have been calculated to identify the major contributing factors that cause

variation in the SR. SR is “Smaller is better” type response which is given by equations 8 and 9 in

Table 11 shows the ANOVA results for S/N ratio of SR at 93 % confidence interval.

most significant factor affecting the SR, followed by

parameters i.e. pulse on

Contribution

% Remark

31.08 S

14.74 S

29.91 S

9.93 NS

1.67 NS

2.92 NS

9.75

100.00

Sq(adj) = 72.4%


percentage contribution of each of the control parameters under study for SR is shown by


are used to calculate mean of S/N ratios at three levels of all factors


Discharge current has the highest


time has the lowest rank. Gap voltage and duty factor were observed to be insignificant in affecting

there is very minute difference as far as the contribution of

Electrode material

Discharge current

Gap voltage

Residual Error

Duty Factor

Pulse on Time



111

electrode material and discharge current is concerned. But the analysis has ranked discharge current

as no1 and electrode material as no.2.

Main effects plot for S/N ratios of SR is shown in the Figure 7. The graph shows that with

increase in discharge current from 8A to 12A, S/N ratio increases. However as the discharge current

increases from 12A to 16A, S/N ratio go on decreasing. The S/N ratio decreases with an increase in

pulse on time. Further as the air pressure is increased S/N ratio increases initially from 4 kg/cm2 to 5

kg/cm2 air pressure and shows a decreasing trend as the air pressure increases to 6 kg/cm

2. It can be

seen that, as the gap voltage increases, S/N ratio shows a decreasing trend. Also as the duty factor

increases, S/N ratio increases. It can be also observed that, Copper-Tungsten electrode has larger S/N

ratio compared to Copper electrode.

Table 12. Response Table for Signal to Noise Ratios of SR

Level Electrode

material Air Pr.

Discharge

current Gap voltage Pulse on time Duty factor

1 -10.86 -12.04 -11.40 -11.12 -11.44 -12.01

2 -12.48 -10.89 -10.86 -11.65 -11.69 -11.59

3 -12.10 -12.75 -12.25 -11.90 -11.42

Delta 1.62 1.21 1.89 1.12 0.46 0.59

Rank 2 3 1 4 6 5

CuCuW

-11.0

-11.5

-12.0

-12.5

-13.0

654 16128

806040

-11.0

-11.5

-12.0

-12.5

-13.0

200150100 1197

Electrode mtl.

Mean of SN ratios

Air pr. Dis. current

gap voltage Pulse on time Duty factor


Data Means

Signal-to-noise: Smaller is better

Fig. 7 Main effects plot for S/N ratios of SR

To conclude the discussion, for minimum SR, the level value with higher S/N ratio of each of

the control parameter under study should be selected at this stage. Thus, a moderate discharge

current of 12A, low pulse on time of 100 µs, low gap voltage of 40 V, higher duty factor of 11,

moderate air pressure of 5 kg/cm2 and copper-tungsten electrode material should be selected.

Thus, it can be concluded that the optimum combination for SR is A1 B2 C2 D1 E1 F3.

This optimal parametric combination is not available in L18 array under study. Hence theoretical

optimum value of SR has to be calculated.

By using the equation 6 from section 4.1, the estimated multi-response signal to noise ratio can

be obtained.



112

nopt = -11.6735+(-10.86+11.6735) + (-10.89+11.6735) + (-10.86+11.6735)

+ (-11.12+11.6735) + (-11.44+11.6735) + (-11.42+11.6735)

nopt = Optimal value of S/N ratio = -8.2225

The corresponding value of SR is given by the equation 10 (see sect.4.2).

Thus, y2 = 6.64125

y opt = 2.577 µm


levels achieved. The experimental result obtained for the SR is 2.668 µm. Thus, the experimental

value agrees reasonably well with prediction. The maximum deviation of predicted result from

experimental result is about 3.53 %. Hence, the experimental result confirms the optimization of SR

using Taguchi method and the resulting model seems to be capable of predicting SR.

5. CONCLUSIONS

1. The MRR, TWR and SR in near dry EDM process are mainly affected by the discharge current

and electrode material.

2. Copper-tungsten electrode material exhibited lower SR and low TWR than that of the copper

electrode but higher MRR was obtained with copper electrode.

3. Increase in the discharge current leads to an increase in the MRR but deteriorating the surface

finish (higher SR values).However, an increase in discharge current initially increases the

TWR but at higher discharge currents TWR was found to be decreasing.

4. The process parameters pulse on time and duty factor were found to be insignificant to affect

the selected responses under study viz. MRR, TWR, and. SR. Air pressure was found to be

significant to affect only SR.

5. Higher material removal rate (MRR) can be achieved with high discharge current and high gap

voltage with copper electrode.

6. Low tool wear rate (TWR) can be achieved with lower discharge current and moderate gap

voltage with copper-tungsten electrode.

7. Low surface roughness (SR) values (Better surface finish) can be achieved with moderate

discharge current and moderate air pressure with copper-tungsten electrode.

6. FUTURE SCOPE

1. The AISI SAE D-2 tool steel has been used as a work-piece material and copper and copper-

tungsten are used as tool electrode materials in the present work. Copper infiltrated graphite

is also a good candidate for tool electrode material. Various combinations of electrode

materials and liquid gas mixtures as dielectric mediums can be tried to check the feasibility of

near dry EDM process for various work piece materials.

2. Various concentrations of liquid in gas can be tried. The combination of additional liquids

such as hydrocarbon oil, and gases such as nitrogen, oxygen, and helium can be tried in near

dry EDM with a goal to tailor unique properties of the EDM dielectric fluid to achieve

machining efficiency and quality improvements, such as high MRR and fine surface

roughness in near dry EDM.

3. The machined surface and subsurface properties, such as microstructure, micro hardness,

residual stress, and material composition, can be investigated to characterize the near-dry

EDM process.

4. The topographical analysis of the machined surfaces by near dry using SEM images can be

done to study the presence of micro-cracks, blowholes and dimples and the surface integrity.



113

5. Multi objective optimization can be done by using techniques like gray relational analysis by

using the same experimental results of the present work.

6. Apart from experimental work, ample scope exists for theoretical modeling and process

simulation in near dry EDM. Current literature is insufficient in this regard.

7. Practical application of the near dry EDM process can bring a lot of advantages for machine

makers and machine end users. Important factor is the simplicity of the machine construction,

not requiring sophisticated and specious dielectric circulation and cooling system. The

design, manufacturing and material costs can be reduced.

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