基于极简粒子群算法的机床主轴结构参数优化
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摘要
为了弥补传统优化方法在机床主轴结构参数优化问题中收敛速度慢、精度不高、需要设置初始值等缺点,提出一种基于极简粒子群优化算法的机床主轴结构参数优化方法。抽象并建立机床主轴结构数学模型,通过实例对fmincon函数法、基本粒子群优化算法和极简粒子群算法的搜索结果进行对比分析。结果显示该改进策略有效地避免了基本粒子群优化算法收敛精度低、后期收敛速度慢等缺点,寻优速度有了大幅度的提高,适合代码容量小,精度要求不高的环境。基于极简粒子群算法的机床主轴结构参数优化方法与fmincon函数法和基本粒子群算法相比,优化结果有了很大提升,且迭代时间分别降低了76.91%和7.31%。
In order to overcome the shortcomings of traditional optimization methods such as slow convergence, low accuracy, and initial value setting in the optimization of the machine-tool spindle parameters, an optimum design of machine-tool spindle parameters based on super-simplify particle swarm optimization(SPSO) algorithm was proposed. The mathematical model of the machine-tool spindle is abstracted and established. The fmincon function method, the PSO and the optimization method proposed in this paper are compared and analyzed through actual cases. The results show that this improved strategy effectively avoids the shortcomings of the PSO such as low computation accuracy and slow convergence speed. The optimization speed is greatly improved, which is very suitable for the environment with small code capacity and low precision. The performance of the optimum design based on SPSO is greatly improved compared with the fmincon function method and PSO, and the iteration time is reduced by 76.91% and 7.31%, respectively.
In order to overcome the shortcomings of traditional optimization methods such as slow convergence, low accuracy, and initial value setting in the optimization of the machine-tool spindle parameters, an optimum design of machine-tool spindle parameters based on super-simplify particle swarm optimization(SPSO) algorithm was proposed. The mathematical model of the machine-tool spindle is abstracted and established. The fmincon function method, the PSO and the optimization method proposed in this paper are compared and analyzed through actual cases. The results show that this improved strategy effectively avoids the shortcomings of the PSO such as low computation accuracy and slow convergence speed. The optimization speed is greatly improved, which is very suitable for the environment with small code capacity and low precision. The performance of the optimum design based on SPSO is greatly improved compared with the fmincon function method and PSO, and the iteration time is reduced by 76.91% and 7.31%, respectively.
引文
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[7] 张景尧,刘志杰,惠东林.基于NSGA-Ⅱ算法的低碳机床主轴多目标优化设计[J].制造技术与机床,2017(12):31-34.
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[10] 刘鸿文.材料力学[M].5版.北京:高等教育出版社,2011.
[2] Shi Y,Eberhart R C.Empirical study of particle swarm optimization[C]// Proceedings of the 1999 Congress on Evolutionary Computation.Piscataway,NJ:IEEE Service Cente,1999:1945-1950.
[3] 顾寄南,熊伟,陈功,等.CK61200车床的关键结构设计及其主轴有限元分析[J].组合机床与自动化加工技术,2016(4):29-32.
[4] 陈忠敏,万庆丰,欧玥,等.CA6140型车床主轴参数优化[J].煤矿机械,2013,34(8):53-55.
[5] 王新旺,蔡家斌,肖齐洪.高频破碎器主轴应力分析及结构优化[J].组合机床与自动化加工技术,2018(1):35-38.
[6] Shen H,Jin L,Xie L M.Optimum Design of Machine-tool Spindle Parameters Based on Matlab[J].Science Technology & Engineering,2008,8(16):4722-4724.
[7] 张景尧,刘志杰,惠东林.基于NSGA-Ⅱ算法的低碳机床主轴多目标优化设计[J].制造技术与机床,2017(12):31-34.
[8] 曾攀.有限元基础教程[M].北京:高等教育出版社,2009.
[9] 赵建峰,魏锋涛,宋俐.基于ANSYS的机床主轴箱结构优化设计[J].机电产品开发与创新,2010,23(5):140-142.
[10] 刘鸿文.材料力学[M].5版.北京:高等教育出版社,2011.