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

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Pages (from-to)883-895
Number of pages13
JournalTransactions of the Canadian Society for Mechanical Engineering
Volume40
Issue number5
Publication statusPublished - 2016 Jan 1

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Particle swarm optimization (PSO)
Surface roughness
Machining

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering

Cite this

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title = "Optimal prediction and design of surface roughness for cnc turning of AL7075-T6 by using the Taguchi hybrid QPSO algorithm",
abstract = "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.",
author = "Wen-Jong Chen and Chuan-Kuei Huang and Yang, {Qi Zheng} and Yang, {Yin Liang}",
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AU - Chen, Wen-Jong

AU - Huang, Chuan-Kuei

AU - Yang, Qi Zheng

AU - Yang, Yin Liang

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N2 - 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.

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