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

被引：0
|
作者：
Gao Y. [1 ]
Ma C. [1 ]
Liu Z. [1 ]
Tian L. [2 ,3 ]
机构：
[1] School of Automotive Studies, Tongji University, Shanghai
[2] Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan
[3] Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan
来源：
Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University | 2020年 / 54卷 / 12期
关键词：
Discrete topology optimization; Genetic algorithm; Multi-objective optimization; Non-dominated sorting;
D O I：
10.16183/j.cnki.jsjtu.2020.99.001
中图分类号：
学科分类号：
摘要：
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. © 2020, Shanghai Jiao Tong University Press. All right reserved.
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页码：1324 / 1334
页数：10
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