基于响应面模型和集成方法的数控机床整机优化
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摘要
针对数控机床整机优化过程中的复杂性,提出了一种响应面模型与集成方法相结合的机床整机优化设计方法。对数控机床整机进行静动态仿真,通过模态试验验证仿真的可信度。采用哈莫雷斯试验设计方法,抽取设计空间的样本点并进行数值模拟和分析计算,建立了整机质量、最大形变量、一阶固有频率、二阶固有频率和三阶固有频率关于整机9个设计变量的响应面模型。提出了基于粒子群算法的初次优化与基于协同优化算法的二次优化相结合的优化策略。先对响应面模型进行初次全局优化,得到Pareto最优解集。在最优解集的基础上再对响应面模型进行二次局部优化,获得最优设计方案。结果表明,优化方案的各项性能都得到了提升,仿真结果验证了优化方案的可信度。因此该优化方法适用于结构较复杂的机床整机的优化设计。
Focusing on complexity of the optimization process of CNC machine tools,an optimization design approach combining response surface model with integrated optimization method is proposed to deal with the optimal design problem for the CNC machines. The static analysis and modal analysis of CNC machine tool is carried out,and the reliability of simulation is verified by modal test. The Hammersley experiment design scheme and the response surface method are combined to construct the response surface models of five performance indexes,i. e.,the total mass,the maximum column deformation,the second-order natural frequency,the first-order natural frequency,and the third-order natural frequency,by fitting with nine design parameters. A quadratic optimization strategy combine first optimization based on particle swarm optimization(PSO) with second optimization based on collaborative optimization(CO) is proposed. First of all,these response surface models are used to obtain the Pareto optimal solution set on global design space by first optimization. Then,on the basis of the optimal solution set,the response surface models are optimized on Local design space by second optimization. The results show that the performance of the optimization scheme is improved,and the analysis results verify the reliability of the optimization scheme. Therefore,the optimization method is applied to the optimization design of the machine tool with complex structure.
Focusing on complexity of the optimization process of CNC machine tools,an optimization design approach combining response surface model with integrated optimization method is proposed to deal with the optimal design problem for the CNC machines. The static analysis and modal analysis of CNC machine tool is carried out,and the reliability of simulation is verified by modal test. The Hammersley experiment design scheme and the response surface method are combined to construct the response surface models of five performance indexes,i. e.,the total mass,the maximum column deformation,the second-order natural frequency,the first-order natural frequency,and the third-order natural frequency,by fitting with nine design parameters. A quadratic optimization strategy combine first optimization based on particle swarm optimization(PSO) with second optimization based on collaborative optimization(CO) is proposed. First of all,these response surface models are used to obtain the Pareto optimal solution set on global design space by first optimization. Then,on the basis of the optimal solution set,the response surface models are optimized on Local design space by second optimization. The results show that the performance of the optimization scheme is improved,and the analysis results verify the reliability of the optimization scheme. Therefore,the optimization method is applied to the optimization design of the machine tool with complex structure.
引文
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[3]于海莲,王永泉,陈花玲,等.响应面模型与多目标遗传算法相结合的机床立柱参数优化[J].西安交通大学学报,2012,46(11):80-85.
[4]Ceruti A,Voloshin V,Marzocca P.Heuristic Algorithms Applied to Multidisciplinary Design Optimization of Unconventional Airship Configuration[J].Journal of Aircraft,2014,51(6):1758-1772.
[5]苏瑞意,桂良进,吴章斌,等.大客车车身骨架多学科协同优化设计[J].机械工程学报,2010,46(18):128-133.
[6]杨丽丽,陈昌亚,王德禹.基于多目标协同优化算法的卫星结构优化设计[J].上海交通大学学报,2014,48(10):1446-1450.
[7]史朝军.基于多学科设计优化的车门结构轻量化研究[D].武汉:武汉科技大学,2014.
[8]Ba爧D,I·smail H Boyac.Modeling and optimization I:Usability of response surface methodology[J].Journal of Food Engineering,2007,78(3):836-845.
[9]Kennedy J,Eberhart R.Particle swarm optimization[C]//IEEE International Conference on Neural Networks,IEEE,1995:1942-1948.
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