INVESTIGATION OF MRR IN WEDM FOR WC-Co SINTERED COMPOSITE

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 –

6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME

349

INVESTIGATION OF MRR IN WEDM FOR WC-Co SINTERED

COMPOSITE

Y.S.Sable 1, R.B.Patil

2, Dr.M.S.Kadam

3

1M.E (Manufacturing) II

nd year student, Department of Mechanical Engineering, Jawaharlal

Nehru Engineering College, Maharashtra-431001, India 2Associate Professor, Department of Mechanical Engineering, Jawaharlal Nehru Engineering

College,Maharashtra-431001 India 3Professor & Head, Department of Mechanical Engineering, Jawaharlal Nehru Engineering

College,Maharashtra-431001 India

ABSTRACT

Tungsten carbide/cobalt (WC/Co) is one of the important composite materials that are

used in the manufacture of cutting tools, mining tools, metal-working tools, dies, and other

special wear resisting applications. It has high hardness and excellent resistance to shock and

wear, and it is not possible to machine easily using conventional techniques. Wire electrical

discharge machining (WEDM) is a best alternative for machining of WC-Co composite into

intricate and complex shapes. Efficient machining of WC-Co composite on WEDM is a

challenging task since it involves large numbers of parameters. Therefore, in present work,

experimental investigation has been carried out to determine the influence of important

WEDM parameters on machining performance of WC-Co composite. Response surface

methodology, which is a collection of mathematical and experimental techniques, was

utilised to obtain the experimental data. Experiments were carried out based on face-centered

central composite design involving five control factors such as pulse on-time, pulse off-time,

peak current, servo voltage and wire tension. Material removal rate were considered as the

measures of performance of the process. A mathematical equation is derived to predict

performance. Surface, response contour plots were utilized to analyze performance. ANOVA

was used to find out the most significant parameters which affect the response characteristics.

Key Words: ANOVA, Material removal rate, Response surface methodology, WEDM, WC-

Co.

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 4, Issue 3, May - June (2013), pp. 349-358 © IAEME: www.iaeme.com/ijmet.asp Journal Impact Factor (2013): 5.7731 (Calculated by GISI) www.jifactor.com

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350

1. INTRODUCTION

With the introduction of new hard materials to be machined, super-hard tool materials

were developed. Among several super-hard tool materials, cemented carbides, especially

WC-Co sintered composites were found to be potential materials to making cutting tools,

metal-working tools, mining tools, dies and other special wear resisting applications. This is

because of its greater hardness, strength and wears resistance over a wide range of

temperatures. WC-Co composite is synthesized by the sintering of WC granules that are held

together by Co binders. Machining of WC-Co composite is very difficult with conventional

machining processes like turning, milling and grinding because of its high hardness and high

melting temperature. Due to the low material removal rate and difficulty in machining of

complex and intricate profiles in WC-Co composite, cost associated with conventional

machining processes is very high [1].

Wire electro-discharge machining process was found to be an extremely powerful

electro-thermal process for machining WC-Co composites with any intricate shape. This

process erodes materials by minute electrical discharges between the wire-electrode (50-300

micron diameter) and the work-piece as shown in Fig. 1. Although WEDM was found to be

potential for machining WC-Co composites, it has some major limitations particularly while

machining this composite. A large difference in melting and evaporation temperatures of it

constituents, makes the process unstable. The melting and evaporation temperatures of

tungsten carbide are 28000C and 6000

0C, respectively while those for Co are 1320

0C and

27000C, respectively. Therefore, Co gets melted, evaporated and removed by the discharge

energy even before the melting of WC. As a result, in absence of a binder (Co) the WC grains

may be released without melting and may lead to unstable machining. Unstable machining

causes short circuit, arcing etc. Hence, it is quite difficult to model such process by analytical

approach based on the physics of this process [2]. There have not been significant research

publications till today on processing of these hard WC-Co composite materials by WEDM.

Thus, there is non availability of machining databases for this type of materials. In absence of

a database, an appropriate model, capable of predicting machining behavior for a wide range

of operating conditions, finds immense utility.

Some attempts have been made to determine optimal machining conditions for WC-

Co composite on EDM and WEDM. [3] Developed a mathematical model and investigates

the effect of WEDM parameter such as Pulse on-time, Pulse off-time and ignition current on

response characteristics such as material removal rate. The authors, in their previous work

[4], have already done an extensive parametric study on this material by WEDM. This

parametric study reveals that there is not available even a single input condition which can

maximize cutting speed and minimize surface roughness or kerf width simultaneously.

Therefore, it is a multi-objective optimization problem. In multi-objective optimization, user

may require several solutions instead of a single one. These solutions are called Pareto-

optimal solutions and they are equally important. Depending upon the requirement of the

user, any one of them can be selected. Say for example, in rough cutting, a process engineer

must select such an input condition from the Pareto-optimal solutions which can provide a

larger cutting speed. This selected input condition may also produce higher kerf width (higher

the kerf width, lower will be the dimensional accuracy). But the process engineer is not

worried for that as his objective is to maximize cutting speed irrespective of any value of kerf

width. Similarly in fine cutting, the selected input condition must be such that it will

minimize kerf width irrespective of any value of cutting speed. As NSGA-II works with a



351

population of points, it may capture several solutions which would be required for the multi-

objective optimization problem. NSGA-II requires a fitness function which is nothing but a

relationship between input and output parameters. As RBFN is making that relationship,

hence, it is coupled with NSGA-II, and thus making a Neuro-Genetic technique.[5] The

researcher developed Neuro Genetic model that predicts results with six control factors pulse

on-time, pulse off-time, peak current, capacitance, gap voltage, and wire feed rate for Cutting

speed, surface roughness and kerf width. [6] The author noticed that grain size of WC and

cobalt composition also shows noticeable influence on machining performance of WC-Co

composite with WEDM. Varying the cobalt concentration and grain size of WC alters the

thermal conductivity of the material, which affects the machining performance.[7] author

studied the influence of EDM parameters on WC-30%Co and developed mathematical model

with electrode rotation, pulse on time, current, and flushing pressure as a input parameter and

Material removal rate and Surface roughness as output parameters.[1] Developed

mathematical model and investigated the multimachining characteristics in WEDM of WC-

5.3%Co composite using a Response surface methodology. four input parameters –pulse-on

time, pulse-off time, servo voltage and wire feed – were investigated for four output

machining characteristics: CS, SR, and RoC.

Because the WEDM involves multi-performance characteristics, the objective of the

present study is to investigate the influence of process parameters and to develop the

mathematical models for one performance characteristics namely Material removal rate in

WEDM of WC-10%Co composite. Response surface methodology with face-centered central

composite design has been utilised to conduct the experimentation which helps to investigate

and to correlate the input parameters with output performance characteristics. Using these

mathematical models, optimal combination of WEDM parameters can be selected for desired

performance characteristics for WC-Co composite.

Fig. 1 Mechanism of WEDM



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2. EXPERIMENTAL WORK

In the present research work, a 5-axis CNC WEDM (Make: Electronica Machine

Tools Ltd., India, Model: SPRINT CUT) was used for the study. Brass wire electrode of 0.25

mm diameter employed as tool electrode, distilled water applied as di-electric fluid. This

study took WC-10% Co sintered composite as the workpiece material. The composition and

the physical properties of the work-piece are given in TABLE 1

TABLE 1 Composition and Physical properties of WC-10% Co

Composition WC-90 wt%, Co 10 wt%

Grain Size 0.7 microfine

Hardness (Hv 30) >1550

Transverse Rupture Strength (N/mm2) >3600

In present investigation, five important WEDM parameters, namely pulse-on time,

pulse-off time, servo voltage and wire tension have been considered with five levels each as

shown in TABLE 2. The parameter range was selected on the basis of pilot experiments and

literature survey and other parameters are kept constant at their default settings.

The response variables selected for this study is Material removal rate (MRR) this can be

calculated using the following expression:

MRR = initial weight – final weight (1)

Machining time

TABLE 2 Process Parameters and their levels

Parameters

Levels

-2 -1 0 +1 +2

Pulse on Time (TON) 106 112 118 124 130

Pulse off Time (TOFF) 30 34 38 42 46

Peak Current (IP) 190 200 210 220 230

Servo Voltage (SV) 4 6 8 10 12

Wire Tension (WT) 15 20 25 32 35

3. EXPERIMENTAL DESIGN USING RSM

In RSM, it is possible to represent independent process parameters in quantitative

form as

y = ƒ (X1, X2, X3 . . . Xn) ± ε (2)

where y is the response (yield), ƒ is the response function, ε is the experimental error, and X1,

X2, X3, . . ., Xn are independent parameters. By plotting the expected response of Y, a



353

surface, known as the response surface, is obtained. The form of f is unknown and may be

very complicated. Thus, RSM aims to approximate f by a suitable lower ordered polynomial

in some region of the independent process variables. If the response can be well modelled by

a linear function of the independent variables, the function (equation (2)) can be written as

y = β0 + β1x1 + β2x2 + ….. + βkxk + Є (3)

However, if a curvature appears in the system, then a higher order polynomial such as the

quadratic model (equation (4)) may be used

K k k

y = β0 + ∑ βi Xi +∑ βii X2ii + ∑ βij XiXj (4)

i=1 i=1 i=1

The objective of using RSM is not only to investigate the response over the entire

factor space, but also to locate the region of interest where the response reaches its optimum

or near optimal value. By studying carefully the response surface model, the combination of

factors that gives the best response, can then be established. The WEDM process was studied

with a standard RSM design, CCD. The MINITAB 16 software was used for regression and

graphical analysis of the data obtained. The optimum values of the selected variables were

obtained by solving the regression equation and by analysing the response surface contour

plot.

4. MATHEMATICAL MODELLING

The 32 experiments according to CCD design were conducted and MRR values were

obtained for each experimental run as listed in TABLE 3. For analysis of the data, to check

the good fit of the model Analysis of Variance is very much required. Model adequacy

checking includes testing for significance of the regression model, for significance on model

coefficients, and for lack of fit. For this purpose, ANOVA is performed.

The modelling was carried out in the following steps:

(a) Identifying the important process control variables and finding their upper and lower

limits;

(b) Developing the design matrix;

(c) Conducting the experiments as per the design matrix;

(d) Recording the response parameters;

(e) Developing regression model;

(f) Checking the adequacy of models;

(g) Testing the significance of coefficients and arriving at the final models;

(h) Presenting the direct and interaction effects of process parameters on MRR and Ra in

graphical form;

(i) Analysis of results.



354

TABLE 3 Experimental results

Trial

No. TON

(µs)

TOFF

(µs)

IP

(A)

SV

(V)

WT

(gram)

MRR

(gms/

min.) actual coded actual coded actual coded actual coded actual coded

1 112 -1 34 -1 200 -1 20 -1 10 1 0.0620

2 124 1 34 -1 200 -1 20 -1 6 -1 0.0833

3 112 -1 42 1 200 -1 20 -1 6 -1 0.0423

4 124 1 42 1 200 -1 20 -1 10 1 0.0690

5 112 -1 34 -1 220 1 20 -1 6 -1 0.0692

6 124 1 34 -1 220 1 20 -1 10 1 0.0958

7 112 -1 42 1 220 1 20 -1 10 1 0.0560

8 124 1 42 1 220 1 20 -1 6 -1 0.0770

9 112 -1 34 -1 200 -1 30 1 6 -1 0.0530

10 124 1 34 -1 200 -1 30 1 10 1 0.0790

11 112 -1 42 1 200 -1 30 1 10 1 0.0390

12 124 1 42 1 200 -1 30 1 6 -1 0.0600

13 112 -1 34 -1 220 1 30 1 10 1 0.0661

14 124 1 34 -1 220 1 30 1 6 -1 0.0860

15 112 -1 42 1 220 1 30 1 6 -1 0.0468

16 124 1 42 1 220 1 30 1 10 1 0.0730

17 106 -2 38 0 210 0 25 0 8 0 0.0421

18 130 2 38 0 210 0 25 0 8 0 0.0890

19 118 0 30 -2 210 0 25 0 8 0 0.0820

20 118 0 46 2 210 0 25 0 8 0 0.0499

21 118 0 38 0 190 -2 25 0 8 0 0.0562

22 118 0 38 0 230 2 25 0 8 0 0.0762

23 118 0 38 0 210 0 15 -2 8 0 0.0720

24 118 0 38 0 210 0 35 2 8 0 0.0590

25 118 0 38 0 210 0 25 0 4 -2 0.0635

26 118 0 38 0 210 0 25 0 12 2 0.0680

27 118 0 38 0 210 0 25 0 8 0 0.0664

28 118 0 38 0 210 0 25 0 8 0 0.0660

29 118 0 38 0 210 0 25 0 8 0 0.0661

30 118 0 38 0 210 0 25 0 8 0 0.0662

31 118 0 38 0 210 0 25 0 8 0 0.0658

32 118 0 38 0 210 0 25 0 8 0 0.0660

The ANOVA of the model for MRR is shown in TABLE 4. The lack-of-fit term is

insignificant, which is desired. The results of the model for MRR are given in TABLE 4. The

value of R2 = 99.94 %, R

2 (adj) = 99.23 %, R

2 (pred.) = 99.90 % which means that the

regression model provides an excellent explanation of the relationship between the

independent variables (factors) and the response (MRR). The associated P-value for the

model is lower than 0.05 (i.e. α = 0.05, or 95 per cent confidence), indicating that the model

is considered to be statistically significant [7].



355

TABLE 4 ANOVA of model

Regression equation is in terms of uncoded parameters:

Material Removal Rate (MRR) = - 0.184152 + 0.001962 * TON - 0.002036 * TOFF +

0.000510 * IP - 0.000648 * SV -0.000652 * WT

Fig. 2 displays the normal probability plot of the residuals for MRR. Notice that the residuals

are falling on a straight line, which means that the errors are normally distributed and the

regression model agree fairly well with the observed values.

0.00100.00050.0000-0.0005-0.0010

99

95

90

80

70

60

50

40

30

20

10

5

1

Residual

Percent

N 32

AD 0.602

P-Value 0.108

Normal Probability Plot(response is MRR)

Fig. 2 Normal probability plot

5. RESULT AND DISCUSSION

From ANOVA TABLE 5 Pulse on time, Pulse off time, Peak current, Servo voltage,

Wire tension are significant parameters for Material removal rate, because their P value<0.05.

In TABLE 5 from the percentage of contribution column it is clear that contribution or effect

of Pulse on time higher 56.96 % and below to that Pulse off time 27.29 % hence most

significant. Peak current and Servo voltage having contribution 10.67%, 4.32%, respectively

hence significant. Wire tension having percentage of contribution is 0.007% hence it is least

significant.

Source DF Seq SS MS F P

Regression 5 0.005833 0.001167 8558.46 0.000 Significant

Residual Error 26 0.000004 0.000000

Lack-of-Fit 21 0.000003 0.000000 3.81 0.071 Not Significant

Pure Error 5 0.000000 0.000000

Total 31 0.005837

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6340(Print), ISSN 0976 – 6359(Online) Volume 4, Issue 3, May

TABLE

Source DF Seq SS

TON 1 0.003325 0.003325

TOFF 1 0.001593 0.001593

IP 1 0.000623 0.000623

SV 1 0.000252 0.000252

WT 1 0.000041 0.000041

Total 31 0.005837

Fig. 3 Percentage

5.1 Response analysis

From Figure 4 (a) the Material removal rate

the increase of pulse on time and at the same time it decreases with the increase of pulse off

time. In WEDM, Material removal rate

flushing of the eroded material o

increases the heat generation at the work surface which increases the

This establishes the fact that Material removal rate

during machining and is dependent not only on the energy contained in a pulse determining

the crater size, but also on the applied energy rate or power.

value decreases the discharge frequency and increases the overall machining time. Also long

pulse-off time at high dielectric flow rate produces the cooling effect on work material and

hence decreases the Material removal rate

rate increases with increase in the peak current values. The higher is the peak current setting,

the larger is the discharge energy. This leads to increase in

peak current setting is too high, wire

Figure 4 (c) that Material removal rate

servo voltage closes the spark gap which results in rapid and large ionization of dielectric

fluid which gives rise to more melting of work material and hence increases the

removal rate. It is seen from Figure 4

wire tension. Increasing wire tension

out of the spark gap and hence increases the

27.29%

10.67%

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976

6359(Online) Volume 4, Issue 3, May - June (2013) © IAEME

356

TABLE 5: Parameter ANOVA for MRR

MS F P % p of

Contribution

0.003325 24393.39 0.000 56.96% Significant

0.001593 11682.31 0.000 27.29% Significant

0.000623 4571.81 0.000 10.67% Significant

0.000252 1845.34 0.000 4.32% Significant

0.000041 299.45 0.000 0.007% Significant

Percentage contribution of parameters

Material removal rate is found to have an increasing trend with


Material removal rate depends on the melting of work surface and then

flushing of the eroded material out of the spark zone. Increasing pulse duration (T

increases the heat generation at the work surface which increases the Material removal rate

Material removal rate is proportional to the energy consumed

and is dependent not only on the energy contained in a pulse determining

the crater size, but also on the applied energy rate or power. Increasing pulse-off time (T


off time at high dielectric flow rate produces the cooling effect on work material and

Material removal rate. It is seen from Figure 4 (b) that Material removal

increases with increase in the peak current values. The higher is the peak current setting,

the larger is the discharge energy. This leads to increase in Material removal rate

peak current setting is too high, wire breakage may occur frequently. It is observed

Material removal rate increases with decrease in servo voltage. Decreasing


rise to more melting of work material and hence increases the

Figure 4(d) that Material removal rate increases with

tension leads to the easy and rapid escape of the eroded ma

out of the spark gap and hence increases the Material removal rate.

56.96%27.29%

10.67%4.32%

%P OF CONTRIBUTION

TON

TOFF

IP

SV


June (2013) © IAEME

Remark

Most

Significant

Most

Significant

Significant

Significant

Least

Significant

is found to have an increasing trend with


depends on the melting of work surface and then

ut of the spark zone. Increasing pulse duration (TON)

Material removal rate.

is proportional to the energy consumed

and is dependent not only on the energy contained in a pulse determining

off time (TOFF)


off time at high dielectric flow rate produces the cooling effect on work material and

Material removal

increases with increase in the peak current values. The higher is the peak current setting,

rate. While the

breakage may occur frequently. It is observed from

voltage. Decreasing


rise to more melting of work material and hence increases the Material

Material removal rate increases with increase in

and rapid escape of the eroded material



357

Fig. 4 (a) Surface Plot of MRR vs Fig.4 (b) Surface Plot of MRR vs

TON, TOFF TON, IP

Fig. 4 (c) Surface Plot of MRR vs Fig.4 (d) Surface Plot of MRR vs

TON, SV TON, WT

6. CONCLUSION

In present work, experimental investigation has been reported on machining

performance of WC-10%Co composite on WEDM. Response surface methodology (RSM), a

statistical technique, has been utilised to investigate the influence of five important WEDM

parameters – pulse on-time, pulse off-time, peak current, servo voltage and wire tension – on

performance characteristics: Material removal rate. Face centered central composite design

was employed to conduct the experiments and to develop a correlation between the WEDM

parameters and each performance characteristics. Analysis of variance (ANOVA) on

experimental data shows that model developed statically significant. ANOVA reveled that

pulse on time, pulse off time are most significant parameters and greatly affected to MRR

whereas, Peak current, servo voltage, wire tension are having less effect on MRR.

Response surface curve shows that increasing pulse on time, peak current, and wire

tension value increasing MRR, and decreasing pulse off time, servo voltage value decreases

MRR.



358

7. ACKNOWLEDGEMENT

The authors would like to thank Dr. S. D. Deshmukh, Principal, JNEC, Aurangabad

for his valuable guidance along with Mr. G. S. Pujari, Vision Tech. Pvt. Ltd, for significant

information and kind support for experimentation and making available their testing facility

for Research Work.

REFERENCES

1) K. Jangra , S. Grover “Modelling and experimental investigation of process

parameters in WEDM of WC-5.3%Co using response surface methodology”

Mechanical Science,3,63–72,2012.

2) P. Saha, P. Saha, S. K. Pal “Parametric Optimization in WEDM of WC-Co Composite

by Neuro-Genetic Technique” Proceedings of the World Congress on Engineering

2011 Vol. III WCE 2011, London, U.K.

3) Muthuraman V, Ramakrishnan R, Karthikeyan L “Modeling and Analysis of MRR in

WEDMed WC-CO Composite by Response Surface Methodology” Indian Journal of

Science and Technology ,December 2012 ,ISSN:0974-6846.

4) Probir Saha, Abhijit Singha, Surjya K. Pal, Partha Saha “Soft computing models

based prediction of cutting speed and surface roughness in wire electro-discharge

machining of tungsten carbide cobalt composite” International Journal of Advance

Manufacturing Technology (2008) 39:74–84.

5) D. Kanagarajan, R. Karthikeyan, K. Palanikumar, J. Paulo Davim, “Optimization of

electrical discharge machining characteristics of WC/Co composites using non

dominated sorting genetic algorithm (NSGA-II)” International Journal of Advance

Manufacturing Technology (2008) 36:1124–1132.

6) Chang-Ho Kim , Jean Pierre Kruth (2001) “Influence of the Electrical Conductivity of

Dielectric on WEDM of Sintered Carbide” KSME International Journal, 15, 1276–

1282, 2001.

7) D Kanagarajan, R Karthikeyan, K Palanikumar, and P Sivaraj “Influence of process

parameters on electric discharge machining of WC/30%Co composites” Journal of

Engineering Manufacture Proceeding 1 Mechanical Engineering (2008) Vol. 222 Part

B-09544054JEM925.

8) Pallavi.H.Agarwal, Prof.Dr.P.M.George and Prof.Dr.L.M.Manocha, “Comparison of

Neural Network Models on Material Removal Rate of C-Sic”, International Journal of

Design and Manufacturing Technology (IJDMT), Volume 3, Issue 1, 2012, pp. 1 - 10,

ISSN Print: 0976 – 6995, ISSN Online: 0976 – 7002.

9) Vishal Francis, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi and Santosh Kumar,

“Application of Taguchi Method and ANOVA in Optimization of Cutting Parameters

for Material Removal Rate and Surface Roughness in Turning Operation”

International Journal of Mechanical Engineering & Technology (IJMET), Volume 4,

Issue 3, 2013, pp. 47 - 53, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

10) S V Subrahmanyam and M. M. M. Sarcar, “Parametric Optimization for Cutting

Speed – A Statistical Regression Modeling for WEDM”, International Journal of

Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 1,

2013, pp. 142 - 150, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

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INVESTIGATION OF MRR IN WEDM FOR WC-Co SINTERED COMPOSITE

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