Modeling and Analysis of Process Parameters on Surface Roughness in EDM of AISI D2 Tool Steel by RSM Approach
In this research, Response Surface Methodology (RSM) is used to investigate the effect of four controllable input variables namely: discharge current, pulse duration, pulse off time and applied voltage Surface Roughness (SR) of on Electrical Discharge Machined surface. To study the proposed second-order polynomial model for SR, a Central Composite Design (CCD) is used to estimation the model coefficients of the four input factors, which are alleged to influence the SR in Electrical Discharge Machining (EDM) process. Experiments were conducted on AISI D2 tool steel with copper electrode. The response is modeled using RSM on experimental data. The significant coefficients are obtained by performing Analysis of Variance (ANOVA) at 5% level of significance. It is found that discharge current, pulse duration, and pulse off time and few of their interactions have significant effect on the SR. The model sufficiency is very satisfactory as the Coefficient of Determination (R2) is found to be 91.7% and adjusted R2-statistic (R2 adj ) 89.6%.
[1] R. Snoeys, F. Staelens, and W. Dekeyser, "Current trends in nonconventional
material removal processes," Ann. CIRP, vol. 35(2), p. 467 480,
1986.
[2] E. C. Jameson, Electrical Discharge Machining. Dearborn, Michigan:
SME, 2001.
[3] O. H. Erzurumlu, T., "Comparison of response surface model with neural
network in determining the surface quality of moulded parts," Materials
and Design, vol. 28, no. 2, pp. 459-465, 2007.
[4] K. Wang, H. L. Gelgele, Y. Wang, Q. Yuan, and M. Fang, "A hybrid
intelligent method for modelling the edm process," International Journal
of Machine Tools and Manufacture, vol. 43, pp. 995-999, Aug 2003.
[5] M. K. Pradhan and C. K. Biswas, "Neuro-fuzzy model on material
removal rate in electrical discharge machining in AISI D2 steel,"
Proceedings of the 2nd International and 23rd All India Manufacturing
Technology, Design and Research Conference, vol. 1, pp. 469-474, 2008.
[6] M. K. Pradhan, R. Das, and C. K. Biswas, "Comparisons of neural
network models on surface roughness in electrical discharge machining,"
Proceedings of the Institution of Mechanical Engineers, Part B: Journal
of Engineering Manufacture, vol. 223, p. (In Press), 2009.
[7] D. Kanagarajan, R. Karthikeyan, K. Palanikumar, and P. Sivaraj,
"Influence of process parameters on electric discharge machining of
WC/30%Co composites," Proceedings of the Institution of Mechanical
Engineers, Part B: Journal of Engineering Manufacture, vol. 222, no. 7,
pp. 807-815, 2008.
[8] A. Jaharah, C. Liang, A. Wahid, S.Z., M. Rahman, and C. Che Hassan,
"Performance of copper electrode in electical discharge machining (edm)
of aisi h13 harden steel," International Journal of Mechanical and
Materials Engineering, vol. 3, no. 1, pp. 25-29, 2008.
[9] P. Kuppan, A. Rajadurai, and S. Narayanan, "Influence of EDM process
parameters in deep hole drilling of inconel 718," International Journal
of Advance Manufacturing Technology, vol. 38, pp. 74-84, 2007.
V (Volt)
Toff (╬╝s)
40 42 44 46 48 50
100
90
80
70
60
50
Ra
2.6 - 3.0
3.0 - 3.4
3.4 - 3.8
3.8 - 4.2
4.2 - 4.6
<
4.6 - 5.0
5.0 - 5.4
> 5.4
2.2
2.2 - 2.6
Fig. 8. Effect of Toff & V on SR
[10] K. Chiang, "Modeling and analysis of the effects of machining parameters
on the performance characteristics in the edm process of al2o
3+tic mixed ceramic," International Journal of Advanced Manufacturing
Technology, vol. 37, no. 5-6, pp. 523-533, 2008.
[11] I. Puertas, C. J. Luis, and L. Alvarez, "Analysis of the influence of
edm parameters on surface quality, mrr and ew of wc-co," Journal of
Materials Processing Technology, vol. 153-154, no. 1-3, pp. 1026-1032,
2004.
[12] J. Rebelo, A. MorA˜ o Dias, R. Mesquita, P. Vassalo, and M. Santos,
"Experimental study on electro-discharge machining and polishing of
high strength copper-beryllium alloys," vol. 103, no. 3, pp. 389-397,
2000.
[13] K.-M. Tsai and P.-J. Wang, "Predictions on surface finish in electrical
discharge machining based upon neural network models," International
Journal of Machine Tools and Manufacture, vol. 41, pp. 1385-1403,
Aug 2001.
[14] G. Petropoulos, N. Vaxevanidis, and C. Pandazaras, "Modeling of
surface finish in electro-discharge machining based upon statistical
multi-parameter analysis," vol. 155-156, no. 1-3, pp. 1247-1251, 2004.
[15] M. K. Pradhan and C. K. Biswas, "Investigations into the effect of process
parameters on MRR in EDM of AISI d2 steel by response surface
methodology," Journal of Mechatronics and Intelligent Manufacturing
(JoMIM),Nova Science Publishers, USA., vol. (Accepted), 2009.
[16] D. C. Montgomery, "Design and analysis of experiments," John willy
and Sons Inc., 2001.
[17] R. L. Mason, R. F., D. Gunst, Texas, and J. L. Hess., Statistical Design
and Analysis of Experiments With Applications to Engineering and
Science. 2nd Edition, A John Wiley & sons publication,, 2003.
[18] Minitab14, Minitab User Manual Release 14 MINITAB Inc,, State
College, PA, USA,, 2003.
[1] R. Snoeys, F. Staelens, and W. Dekeyser, "Current trends in nonconventional
material removal processes," Ann. CIRP, vol. 35(2), p. 467 480,
1986.
[2] E. C. Jameson, Electrical Discharge Machining. Dearborn, Michigan:
SME, 2001.
[3] O. H. Erzurumlu, T., "Comparison of response surface model with neural
network in determining the surface quality of moulded parts," Materials
and Design, vol. 28, no. 2, pp. 459-465, 2007.
[4] K. Wang, H. L. Gelgele, Y. Wang, Q. Yuan, and M. Fang, "A hybrid
intelligent method for modelling the edm process," International Journal
of Machine Tools and Manufacture, vol. 43, pp. 995-999, Aug 2003.
[5] M. K. Pradhan and C. K. Biswas, "Neuro-fuzzy model on material
removal rate in electrical discharge machining in AISI D2 steel,"
Proceedings of the 2nd International and 23rd All India Manufacturing
Technology, Design and Research Conference, vol. 1, pp. 469-474, 2008.
[6] M. K. Pradhan, R. Das, and C. K. Biswas, "Comparisons of neural
network models on surface roughness in electrical discharge machining,"
Proceedings of the Institution of Mechanical Engineers, Part B: Journal
of Engineering Manufacture, vol. 223, p. (In Press), 2009.
[7] D. Kanagarajan, R. Karthikeyan, K. Palanikumar, and P. Sivaraj,
"Influence of process parameters on electric discharge machining of
WC/30%Co composites," Proceedings of the Institution of Mechanical
Engineers, Part B: Journal of Engineering Manufacture, vol. 222, no. 7,
pp. 807-815, 2008.
[8] A. Jaharah, C. Liang, A. Wahid, S.Z., M. Rahman, and C. Che Hassan,
"Performance of copper electrode in electical discharge machining (edm)
of aisi h13 harden steel," International Journal of Mechanical and
Materials Engineering, vol. 3, no. 1, pp. 25-29, 2008.
[9] P. Kuppan, A. Rajadurai, and S. Narayanan, "Influence of EDM process
parameters in deep hole drilling of inconel 718," International Journal
of Advance Manufacturing Technology, vol. 38, pp. 74-84, 2007.
V (Volt)
Toff (╬╝s)
40 42 44 46 48 50
100
90
80
70
60
50
Ra
2.6 - 3.0
3.0 - 3.4
3.4 - 3.8
3.8 - 4.2
4.2 - 4.6
<
4.6 - 5.0
5.0 - 5.4
> 5.4
2.2
2.2 - 2.6
Fig. 8. Effect of Toff & V on SR
[10] K. Chiang, "Modeling and analysis of the effects of machining parameters
on the performance characteristics in the edm process of al2o
3+tic mixed ceramic," International Journal of Advanced Manufacturing
Technology, vol. 37, no. 5-6, pp. 523-533, 2008.
[11] I. Puertas, C. J. Luis, and L. Alvarez, "Analysis of the influence of
edm parameters on surface quality, mrr and ew of wc-co," Journal of
Materials Processing Technology, vol. 153-154, no. 1-3, pp. 1026-1032,
2004.
[12] J. Rebelo, A. MorA˜ o Dias, R. Mesquita, P. Vassalo, and M. Santos,
"Experimental study on electro-discharge machining and polishing of
high strength copper-beryllium alloys," vol. 103, no. 3, pp. 389-397,
2000.
[13] K.-M. Tsai and P.-J. Wang, "Predictions on surface finish in electrical
discharge machining based upon neural network models," International
Journal of Machine Tools and Manufacture, vol. 41, pp. 1385-1403,
Aug 2001.
[14] G. Petropoulos, N. Vaxevanidis, and C. Pandazaras, "Modeling of
surface finish in electro-discharge machining based upon statistical
multi-parameter analysis," vol. 155-156, no. 1-3, pp. 1247-1251, 2004.
[15] M. K. Pradhan and C. K. Biswas, "Investigations into the effect of process
parameters on MRR in EDM of AISI d2 steel by response surface
methodology," Journal of Mechatronics and Intelligent Manufacturing
(JoMIM),Nova Science Publishers, USA., vol. (Accepted), 2009.
[16] D. C. Montgomery, "Design and analysis of experiments," John willy
and Sons Inc., 2001.
[17] R. L. Mason, R. F., D. Gunst, Texas, and J. L. Hess., Statistical Design
and Analysis of Experiments With Applications to Engineering and
Science. 2nd Edition, A John Wiley & sons publication,, 2003.
[18] Minitab14, Minitab User Manual Release 14 MINITAB Inc,, State
College, PA, USA,, 2003.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:58428", author = "M. K. Pradhan and C. K. Biswas", title = "Modeling and Analysis of Process Parameters on Surface Roughness in EDM of AISI D2 Tool Steel by RSM Approach", abstract = "In this research, Response Surface Methodology (RSM) is used to investigate the effect of four controllable input variables namely: discharge current, pulse duration, pulse off time and applied voltage Surface Roughness (SR) of on Electrical Discharge Machined surface. To study the proposed second-order polynomial model for SR, a Central Composite Design (CCD) is used to estimation the model coefficients of the four input factors, which are alleged to influence the SR in Electrical Discharge Machining (EDM) process. Experiments were conducted on AISI D2 tool steel with copper electrode. The response is modeled using RSM on experimental data. The significant coefficients are obtained by performing Analysis of Variance (ANOVA) at 5% level of significance. It is found that discharge current, pulse duration, and pulse off time and few of their interactions have significant effect on the SR. The model sufficiency is very satisfactory as the Coefficient of Determination (R2) is found to be 91.7% and adjusted R2-statistic (R2 adj ) 89.6%.
", keywords = "Electrical discharge machining, surface roughness, response surface methodology, ANOVA, central composite design.", volume = "3", number = "9", pages = "1087-6", }
