摘要
针对复杂机床结构优化中,通常难以获得设计变量与性能目标之间显式函数关系式的问题,提出了一种基于RBF神经网络模型和组合优化策略的结构优化设计方法。以某型精密数控车床主轴箱为研究对象,通过有限元软件ANSYS Workbench和多学科优化软件Isight联合仿真技术对主轴箱设计尺寸进行最优拉丁超立方实验设计和灵敏度分析,根据实验样本点构建RBF神经网络模型代替主轴箱有限元模型。采用多岛遗传算法(MIGA)和序列二次规划法(NLPQL)相结合的组合优化策略,对RBF神经网络模型进行优化设计。优化结果表明,在保证主轴箱静动态性能的前提下,质量减轻12.89%,达到了预期的效果。
A structural optimization design method based on an RBF neural network model and combined optimization strategy was proposed in order to obtain the explicit functional relationship equation between design variables and performance objectives in the structural optimization of complex machine tools.Taking a type of precision lathe spindle box as the research object,the optimal Latin hypercube experimental design and sensitivity analysis of the spindle box design dimensions was carried out using the joint simulation technology of finite element software ANSYS Workbench and multidisciplinary optimization software Insight and the RBF neural network model was constructed in addition to the spindle box finite element model according to the experimental sample points.A combined optimization strategy combining multi-island genetic algorithm(MIGA)and sequential quadratic programming method(NLPQL)is used to optimize the design of the RBF neural network model.The optimization results show that the mass is reduced by 12.89%while the static and dynamic performance of the spindle box is guaranteed,which achieves the expected results in a more efficient way than the standard optimization.
作者
乔雪涛
周世涛
李优华
曹康
盛坤
张洪伟
QIAO Xuetao;ZHOU Shitao;LI Youhua;CAO Kang;SHENG Kun;ZHANG Hongwei(School of Mechanical&Electronic Engineering,Zhongyuan University of Technology,Zhengzhou 450007,CHN)
出处
《制造技术与机床》
北大核心
2023年第5期98-104,共7页
Manufacturing Technology & Machine Tool
基金
河南省科技攻关项目(202102210276、212102210318)。
关键词
主轴箱
RBF神经网络模型
组合优化策略
最优拉丁超立方实验设计
spindle box
RBF neural network model
combination optimization strategy
optimal lardin hypercube experimental design
