高云凯¹, 马超¹(), 刘哲¹, 田林雳^2,3

^1.同济大学　汽车学院，上海 　201804
^2.武汉理工大学　现代汽车零部件技术湖北省重点实验室，武汉 　430070
^3.武汉理工大学　汽车零部件技术湖北省协同创新中心，武汉 　430070

收稿日期:2019-12-04出版日期:2020-12-01发布日期:2020-12-31
通讯作者:马超E-mail:machaomit@163.com
作者简介:高云凯(1963-)，男，黑龙江省哈尔滨市人，教授，博士生导师，主要研究方向为车身结构分析与优化设计．
基金资助:国家重点研发计划新能源汽车重点专项(2016YFB0101602);国家自然科学基金(51575399)

Discrete Topology Optimization of Body-in-White Welding Production Platform Based on NSGA-III

GAO Yunkai¹, MA Chao¹(), LIU Zhe¹, TIAN Linli^2,3

^1.School of Automotive Studies, Tongji University, Shanghai 201804, China
^2.Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
^3.Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan 430070, China

Received:2019-12-04Online:2020-12-01Published:2020-12-31
Contact:MA Chao E-mail:machaomit@163.com

摘要/Abstract

摘要： 针对第3代非支配排序遗传算法 (NSGA-III)求解离散拓扑优化收敛性差的问题，提出一种改进的NSGA-III (mNSGA-III)，用于某白车身焊装生产平台的结构优化．提出新型极值点选择算法，以稳定种群中个体的归一化过程．建立该生产平台的有限元模型，并以其总质量、最大应力和多个节点的z向位移最小为目标，进行离散拓扑优化．利用MATLAB集成MSC.Nastran软件，二次开发离散拓扑优化程序．通过优化、筛选，获得合理的杆件布置设计方案，使得结构总质量降低了30.1%，且刚度和强度均符合标准．优化结果表明，mNSGA-III 在求解高维多目标离散拓扑优化方面，具有计算过程稳定、收敛速度较快等优点，为大型钢结构的优化设计提供了新方法，具备工程实际应用价值．
关键词: 离散拓扑优化, 多目标优化, 非支配排序, 遗传算法
Abstract: This paper proposes a modified third generation non-dominated sorting genetic algorithm (mNSGA-III) to overcome the poor convergence of third generation non-dominated sorting genetic algorithm (NSGA-III) in handling discrete topology optimization. It uses the mNSGA-III for the structural optimization of a body-in-white (BIW) welding production platform. It proposes an advanced extreme point selection to stabilize the normalization of populations. It constructs the finite element model of BIW welding production platform. Using discrete topology optimization, it treats the total mass, maximum stress and z-direction displacements of several nodes of platform as objective functions. It developed a discrete topology optimization program by using MATLAB interfaced the commercial finite element code MSC.Nastran. Finally, it selected the design with appropriate layout in view of stiffness and strength of the structure. The optimal design conforms to the design standards and the total mass reduces by 30.1%. The results show that mNSGA-III gets a more stable optimization process and easy to converge when solving the multi-objective discrete topology optimization problems. The proposed method provides an applicable method for the optimization of giant steel structures and has great values for practical engineering problem.
Key words: discrete topology optimization, multi-objective optimization, non-dominated sorting, genetic algorithm


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