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 language | English |
|---|---|
| Pages (from-to) | 883-895 |
| Number of pages | 13 |
| Journal | Transactions of the Canadian Society for Mechanical Engineering |
| Volume | 40 |
| Issue number | 5 |
| Publication status | Published - 2016 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Mechanical Engineering
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Optimal prediction and design of surface roughness for cnc turning of AL7075-T6 by using the Taguchi hybrid QPSO algorithm. / Chen, Wen-Jong; Huang, Chuan-Kuei; Yang, Qi Zheng; Yang, Yin Liang.
In: Transactions of the Canadian Society for Mechanical Engineering, Vol. 40, No. 5, 01.01.2016, p. 883-895.Research output: Contribution to journal › Article
TY - JOUR
T1 - Optimal prediction and design of surface roughness for cnc turning of AL7075-T6 by using the Taguchi hybrid QPSO algorithm
AU - Chen, Wen-Jong
AU - Huang, Chuan-Kuei
AU - Yang, Qi Zheng
AU - Yang, Yin Liang
PY - 2016/1/1
Y1 - 2016/1/1
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.
AB - 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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UR - http://www.scopus.com/inward/citedby.url?scp=85018445557&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:85018445557
VL - 40
SP - 883
EP - 895
JO - Transactions of the Canadian Society for Mechanical Engineering
JF - Transactions of the Canadian Society for Mechanical Engineering
SN - 0315-8977
IS - 5
ER -