NDLI: An Investigation of Grinding Process Optimization via Evolutionary Algorithms

Content Provider	IEEE Xplore Digital Library
Author	Lee, T.S. Ting, T.O. Lin, Y.J.
Copyright Year	2007
Description	Author affiliation: Fac. of Eng. & Technol., Multimedia Univ., Malacca (Lee, T.S.)
Abstract	In this paper, the performance of some evolutionary algorithms on grinding process optimization of silicon carbide (SiC) is investigated. The grinding of SiC is not an easy task due to its low fracture toughness, therefore making the material sensitive to cracking. The efficient grinding involves the optimal selection of operating parameters to maximize the material removal rate (MRR) while maintaining the required surface finish and limiting surface damage. In this work, optimization based on the available model has been carried out to obtain optimum parameters for silicon carbide grinding via three prominent evolutionary algorithms. They are particle swarm optimization (PSO), differential evolution (DE) and genetic algorithm (GA). The objective of this optimization process is to maximize the MRR, subject to surface finish and damage constraints of the grinding process. Numerical results show that PSO is comparatively superior in comparison with DE and GA algorithms for grinding process optimization in terms of its accuracy and convergent capability
Starting Page	176
Ending Page	181
File Size	361783
Page Count	6
File Format	PDF
ISBN	1424407087
DOI	10.1109/SIS.2007.368043
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-04-01
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Silicon carbide Evolutionary computation Machining Thermal conductivity Surface roughness Ceramics Rough surfaces Surface finishing Surface cracks Genetic algorithms
Content Type	Text
Resource Type	Article

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