Title: An effective approach for the optimisation of cutting parameters

Authors: Xiaoyun Jiang; Wenchin Chen

Addresses: School of Management, Xiamen University of Technology, No. 600, Ligong Rd., Jimei District, Xiamen, China ' Department of Industrial Management, Chung Hua University, No. 707, Sec. 2, WuFu Rd., Hsinchu, Taiwan

Abstract: Optimisation of cutting parameters enhances the precision and stability of processes in the machinery industry. In this study, hole-boring in bearing brackets for automobiles is examined as a case for optimisation, and five cutting parameters having great influence on the workpiece cutting accuracy are selected. To optimise the cutting parameters, a novel approach integrating Taguchi method, particle swarm optimisation (PSO) and back-propagation neural networks based on PSO is presented in this study. Experimental results show that the proposed approach can quickly determine the optimal cutting parameters, which not only meet the quality specification for the hole-boring, but also effectively enhance the overall process stability.

Keywords: backpropagation neural networks; cutting parameters; MATLAB; PSO; particle swarm optimisation; Taguchi methods; hole boring; bearing brackets; automobile industry; automotive manufacturing; process stability.

DOI: 10.1504/IJCAT.2014.066723

International Journal of Computer Applications in Technology, 2014 Vol.50 No.3/4, pp.180 - 185

Published online: 07 Feb 2015 *

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