An effective approach for the optimisation of cutting parameters

被引：1
作者：
Jiang, Xiaoyun [1 ]
Chen, Wenchin [2 ]
机构：
[1] School of Management, Xiamen University of Technology, No. 600, Ligong Rd., Jimei District, Xiamen
[2] Department of Industrial Management, Chung Hua University, No. 707, WuFu Rd., Hsinchu
来源：
International Journal of Computer Applications in Technology | 2014年 / 50卷 / 03期
关键词：
Back-propagation neural network; Cutting parameter; MATLAB; Particle swarm optimisation; PSO; Taguchi method;
D O I：
10.1504/IJCAT.2014.066723
中图分类号：
学科分类号：
摘要：
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. Copyright © 2014 Inderscience Enterprises Ltd.
引用
收藏
页码：180 / 185
页数：5
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