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
N1 - Funding Information:
This work is supported by the Ministry of Science and Technology in Taiwan (MOST 103-2221-E-018-008).
PY - 2016
Y1 - 2016
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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U2 - 10.1139/tcsme-2016-0072
DO - 10.1139/tcsme-2016-0072
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 -