Modeling and Optimization of Process Parameters in PMEDM by Genetic Algorithm
This paper addresses modeling and optimization of process parameters in powder mixed electrical discharge machining (PMEDM). The process output characteristics include metal removal rate (MRR) and electrode wear rate (EWR). Grain size of Aluminum powder (S), concentration of the powder (C), discharge current (I) pulse on time (T) are chosen as control variables to study the process performance. The experimental results are used to develop the regression models based on second order polynomial equations for the different process characteristics. Then, a genetic algorithm (GA) has been employed to determine optimal process parameters for any desired output values of machining characteristics.
[1] H. Zarepour, A.F. Tehrani, D. Karimi, and S. Amini, "Statistical
analysis on electrode wear in EDM of tool steel DIN 1.2714 used in
forging dies," J. Mater Process Tech., vol. 188, pp. 711-714, 2007.
[2] A.Ghosh, A.K. Mallik, Manufacturing Science, Affiliated East-West
Press, New Delhi, 1991.
[3] B.H. Yan, H.C. Tsai, F.Y. "Huang, The effect of EDM of a dielectric
of a urea solution in water on modifying the surface of titanium". Int.
J. Mach. Manuf., vol. 45, no. 2, pp. 194-200, 2005.
[4] K.H. Ho, S.T. Newman, "State of art electrical discharge machining
(EDM)", Int. J. Mach. Manuf. Vol. 43, no 13,pp 1287-1300, 2003.
[5] Puertas, C.J. Luis, "A study on the machining parameters
optimization of electrical discharge machining", J. Mater. Process
Tech , vol. 144, pp. 521-526, 2003.
[6] M.M. Schwartz, Engineering applications of ceramic materials,
American Society for Metals, Metals Park, Ohio. 1995.
[7] F.K. locke, "Modern approaches for the production of ceramic
components", J. Eur. Ceram. Soc., vol. 17, pp. 457-465, 1997.
[8] J.T. Huang and Y.S. Liao, "Optimization of machining parameters of
wire-EDM based on grey relational and statistical analysis", Int. J.
Prodn. Res. vol. 41, no. 8, pp. 1707-1720, 2003.
[9] K.Y. Kung, J.T. Horng, and K.T. Chiang, "Material removal rate and
electrode wear ratio study on the powder mixed electrical discharge
machining of cobalt-bonded tungsten carbide", Int. J. Adv Manuf.
Technol., to be published.
[10] D.C. Montgomery, E.A. Peck, G.G. Vining, Introduction to Linear
Regression Analysis, Third ed., Wiley, New York, 2003.
[11] G.R. Cheng, Genetic algorithm and engineering design, New York:
John Wiley & Sons, 1997.
[12] J.C. Su , J.Y. Kao, and Y.S. Tarng, "Optimization of the electrical
discharge machining process using a GA-based neural network". Int.
J. Adv. Manuf. Tech., vol. 24, pp 81-90, 2004.
[1] H. Zarepour, A.F. Tehrani, D. Karimi, and S. Amini, "Statistical
analysis on electrode wear in EDM of tool steel DIN 1.2714 used in
forging dies," J. Mater Process Tech., vol. 188, pp. 711-714, 2007.
[2] A.Ghosh, A.K. Mallik, Manufacturing Science, Affiliated East-West
Press, New Delhi, 1991.
[3] B.H. Yan, H.C. Tsai, F.Y. "Huang, The effect of EDM of a dielectric
of a urea solution in water on modifying the surface of titanium". Int.
J. Mach. Manuf., vol. 45, no. 2, pp. 194-200, 2005.
[4] K.H. Ho, S.T. Newman, "State of art electrical discharge machining
(EDM)", Int. J. Mach. Manuf. Vol. 43, no 13,pp 1287-1300, 2003.
[5] Puertas, C.J. Luis, "A study on the machining parameters
optimization of electrical discharge machining", J. Mater. Process
Tech , vol. 144, pp. 521-526, 2003.
[6] M.M. Schwartz, Engineering applications of ceramic materials,
American Society for Metals, Metals Park, Ohio. 1995.
[7] F.K. locke, "Modern approaches for the production of ceramic
components", J. Eur. Ceram. Soc., vol. 17, pp. 457-465, 1997.
[8] J.T. Huang and Y.S. Liao, "Optimization of machining parameters of
wire-EDM based on grey relational and statistical analysis", Int. J.
Prodn. Res. vol. 41, no. 8, pp. 1707-1720, 2003.
[9] K.Y. Kung, J.T. Horng, and K.T. Chiang, "Material removal rate and
electrode wear ratio study on the powder mixed electrical discharge
machining of cobalt-bonded tungsten carbide", Int. J. Adv Manuf.
Technol., to be published.
[10] D.C. Montgomery, E.A. Peck, G.G. Vining, Introduction to Linear
Regression Analysis, Third ed., Wiley, New York, 2003.
[11] G.R. Cheng, Genetic algorithm and engineering design, New York:
John Wiley & Sons, 1997.
[12] J.C. Su , J.Y. Kao, and Y.S. Tarng, "Optimization of the electrical
discharge machining process using a GA-based neural network". Int.
J. Adv. Manuf. Tech., vol. 24, pp 81-90, 2004.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:55151", author = "Farhad Kolahan and Mohammad Bironro", title = "Modeling and Optimization of Process Parameters in PMEDM by Genetic Algorithm", abstract = "This paper addresses modeling and optimization of process parameters in powder mixed electrical discharge machining (PMEDM). The process output characteristics include metal removal rate (MRR) and electrode wear rate (EWR). Grain size of Aluminum powder (S), concentration of the powder (C), discharge current (I) pulse on time (T) are chosen as control variables to study the process performance. The experimental results are used to develop the regression models based on second order polynomial equations for the different process characteristics. Then, a genetic algorithm (GA) has been employed to determine optimal process parameters for any desired output values of machining characteristics.
", keywords = "Regression modeling, PMEDM, GeneticAlgorithm, Optimization.", volume = "2", number = "12", pages = "1322-5", }
