Optimal prediction and design of surface roughness for cnc turning of AL7075-T6 by using the Taguchi hybrid QPSO algorithm

Wen Jong Chen, Chuan Kuei Huang, Qi Zheng Yang, Yin Liang Yang

研究成果: Article

1 引文 斯高帕斯(Scopus)

摘要

This paper combines the Taguchi-based response surface methodology (RSM) with a multi-objective hybrid quantum-behaved particle swarm optimization (MOHQPSO) to predict the optimal surface roughness of Al7075-T6 workpiece through a CNC turning machining. First, the Taguchi orthogonal array L27 (36) was applied to determine the crucial cutting parameters: feed rate, tool relief angle, and cutting depth. Subsequently, the RSM was used to construct the predictive models of surface roughness (Ra, Rmax, and Rz). Finally, the MOHQPSO with mutation was used to determine the optimal roughness and cutting conditions. The results show that, compared with the non-optimization, Taguchi and classical multi-objective particle swarm optimization methods (MOPSO), the roughness Ra using MOHQPSO along the Pareto optimal solution are improved by 68.24, 59.31 and 33.80%, respectively. This reveals that the predictive models established can improve the machining quality in CNC turning of Al7075-T6.

原文English
頁(從 - 到)883-895
頁數13
期刊Transactions of the Canadian Society for Mechanical Engineering
40
發行號5
DOIs
出版狀態Published - 2016 一月 1

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

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