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Neuro-fuzzy Model and Regression Model a Comparison Study of MRR in Electrical Discharge Machining of D2 Tool Steel

Authors: M. K. Pradhan, C. K. Biswas,


In the current research, neuro-fuzzy model and regression model was developed to predict Material Removal Rate in Electrical Discharge Machining process for AISI D2 tool steel with copper electrode. Extensive experiments were conducted with various levels of discharge current, pulse duration and duty cycle. The experimental data are split into two sets, one for training and the other for validation of the model. The training data were used to develop the above models and the test data, which was not used earlier to develop these models were used for validation the models. Subsequently, the models are compared. It was found that the predicted and experimental results were in good agreement and the coefficients of correlation were found to be 0.999 and 0.974 for neuro fuzzy and regression model respectively

Keywords: Electrical discharge machining, material removal rate, neuro-fuzzy model, regression model, mountain clustering.

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[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] K. H. Ho and S. T. Newman, "State of the art electrical discharge machining (edm)," International Journal of Machine Tools and Manufacture, vol. 43, pp. 1287-1300, Oct 2003.
[3] 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.
[4] G. V. C.J. Luis, I. Puertas ., "Material removal rate and electrode wear study on the EDM of silicon carbide," Journal of Materials Processing Technology, vol. 164-165, pp. 889-896, 2005.
[5] J. Valentincic and M. Junkar, "On-line selection of rough machining parameters," Journal of Materials Processing Technology, vol. 149, pp. 256-262, Jun 2004.
[6] P. Wang and K. Tsai, "Semi-empirical model on work removal and tool wear in electrical discharge machining," Journal of Materials Processing Technology, vol. 114, no. 1, pp. 1-17, 2001, cited By (since 1996): 11.
[7] 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.
[8] D. K. Panda and R. K. Bhoi, "Artificial neural network prediction of material removal rate in electro- discharge machining," Materials and Manufacturing Processes, vol. 20, pp. 645-672., 2005.
[9] 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. Inpress, 2009.
[10] R. Yager and D. Filev, "Approximate clustering by the mountain clustering,," IEEE Transactions on Systems Man and Cybernetics,, vol. 24,, pp. 338-358., 1994.
[11] ÔÇöÔÇö, Essentials of Fuzzy Modeling and Control. New York: John Wiley & Sons, Inc, 1995.
[12] D. D. Dibitono, P. T. Eubank, M. R. Patel, and M. A. Barrufet, "Theoretical model of the electrical discharge machining process i. a simple cathode erosion model,," Journal of Applied Physics,, vol. 66, pp. 4095-4103, 1989.