Title: Evolutionary algorithms on diamond grinding of SiC

Authors: T.S. Lee, T.O. Ting, P.V. Rao, V.C. Venkatesh, Y.J. Lin

Addresses: Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450 Malacca, Malaysia. ' Department of Information Technology, HKUSpace Global College, No. 1 Ren Ai Road, Dushu Lake Higher Education Town, Suzhou Industrial Park, 215123 China. ' Department of Mechanical Engineering, Indian Institute of Technology Delhi, New Delhi, 110016 India. ' Department of Mechanical Engineering, University of Nevada-Las Vegas, 4505 Maryland Parkway, Las Vegas, NV 89154-4027, USA. ' Department of Mechanical Engineering, University of Texas, Tyler, TX 75799, USA

Abstract: Optimisation is needed in order to achieve better product quality, high productivity and low cost. Efficient grinding involves the optimal selection of operating parameters to maximise the material removal rate (MRR) while maintaining the required surface finish, and minimising surface damage. Three prominent evolutionary algorithms were used. They are particle Swarm optimisation (PSO), differential evolution (DE) and genetic algorithm (GA). Novel methodologies for tuning PSO and DE for optimising grinding process have been proposed. Validation of optimum MRR obtained from these algorithms has been done and the MRR values obtained from the proposed methodologies are encouraging.

Keywords: ceramics grinding; constrained optimisation; particle swarm optimisation; PSO; differential evolution; genetic algorithms; GAs; silicon carbide; diamond grinding; material removal rate; MRR; surface quality.

DOI: 10.1504/IJPTECH.2011.038105

International Journal of Precision Technology, 2011 Vol.2 No.1, pp.1 - 11

Published online: 18 Jan 2011 *

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