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

doi:10.16183/j.cnki.jsjtu.2020.99.001

Abstract

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

CLC Number:

GAO Yunkai, MA Chao, LIU Zhe, TIAN Linli. Discrete Topology Optimization of Body-in-White Welding Production Platform Based on NSGA-III[J]. Journal of Shanghai Jiao Tong University, 2020, 54(12): 1324-1334.

Figures/Tables 17

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Tab.1

Fig.9

Fig.10

Fig.11

Fig.12

Fig.13

Fig.14

Fig.15

Tab.2

References 20

[1]	FRANS R, ARFIADI Y. Sizing, shape, and topology optimizations of roof trusses using hybrid genetic algorithms[J]. Procedia Engineering, 2014, 95: 185-195.
[2]	梁俊毅，张建龙，马雪瑞，等. 基于多混沌算子遗传算法的混合动力汽车控制策略优化[J]. 上海交通大学学报，2015, 49(4): 442-449.
	LIANG Junyi, ZHANG Jianlong, MA Xuerui, et al. Control strategy optimization for hybrid electric vehicle based on multi-chaotic operators genetic algorithm[J]. Journal of Shanghai Jiao Tong University, 2015, 49(4): 442-449.
[3]	田亮，罗宇，王阳. 基于遗传算法优化BP神经网络的TIG焊缝尺寸预测模型[J]. 上海交通大学学报，2013, 47(11): 1690-1696.
	TIAN Liang, LUO Yu, WANG Yang. Prediction model of TIG welding seam size based on BP neural network optimized by genetic algorithm[J]. Journal of Shanghai Jiao Tong University, 2013, 47(11): 1690-1696.
[4]	PAN J, WANG D Y. Topology optimization of truss structure with fundamental frequency and frequency domain dynamic response constraints[J]. Acta Mechanica Solida Sinica, 2006, 19(3): 231-240.
[5]	ASSIMI H, JAMALI A, NARIMAN-ZADEH N. Sizing and topology optimization of truss structures using genetic programming[J]. Swarm and Evolutionary Computation, 2017, 37: 90-103.
[6]	孙仁范，牟在根，颜谋，等. 遗传算法在桁架结构优化设计中的应用[J]. 建筑结构学报，2004, 25(3): 75-79.
	SUN Renfan, MU Zaigen, YAN Mou, et al. Application of genetic algorithms in optimums design of truss structures[J]. Journal of Building Structures, 2004, 25(3): 75-79.
[7]	WANG H G, OHMORI H. Elasto-plastic analysis based truss optimization using Genetic Algorithm[J]. Engineering Structures, 2013, 50: 1-12.
[8]	梁笑天，袁行飞，李阿龙. 索穹顶结构多目标形状优化设计[J]. 华中科技大学学报(自然科学版), 2016, 44(7): 110-115.
	LIANG Xiaotian, YUAN Xingfei, LI Along. Multi-objective shape optimization design of cable dome[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2016, 44(7): 110-115.
[9]	王平，郑松林，吴光强. 基于协同优化和多目标遗传算法的车身结构多学科优化设计[J]. 机械工程学报，2011, 47(2): 102-108.
	WANG Ping, ZHENG Songlin, WU Guangqiang. Multidisciplinary design optimization of vehicle body structure based on collaborative optimization and multi-objective genetic algorithm[J]. Journal of Mechanical Engineering, 2011, 47(2): 102-108.
[10]	DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[11]	高云凯，段少东. 基于NSGA-II 算法的客车底架的离散拓扑优化[J]. 同济大学学报(自然科学版), 2017, 45(11): 1664-1669.
	GAO Yunkai, DUAN Shaodong. Discrete topology optimization of bus chassis frame based on NSGA-II[J]. Journal of Tongji University (Natural Science), 2017, 45(11): 1664-1669.
[12]	HO-HUU V, DUONG-GIA D, VO-DUY T, et al. An efficient combination of multi-objective evolutionary optimization and reliability analysis for reliability-based design optimization of truss structures[J]. Expert Systems with Applications, 2018, 102: 262-272.
[13]	胡浩，李刚. 演化算法求解桁架多目标拓扑优化的收敛速度研究[J]. 计算力学学报，2015, 32(3): 301-306.
	HU Hao, LI Gang. A convergence speed study on evolutionary algorithms for solving truss multi-objective topology optimization[J]. Chinese Journal of Computational Mechanics, 2015, 32(3): 301-306.
[14]	YUAN Y, XU H, WANG B, et al. Balancing convergence and diversity in decomposition-based many-objective optimizers[J]. IEEE Transactions on Evolutionary Computation, 2016, 20(2): 180-198.
[15]	DEB K, JAIN H. An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach. Part I: Solving problems with box constraints[J]. IEEE Transactions on Evolutionary Computation, 2014, 18(4): 577-601.
[16]	DAS I, DENNIS J. Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems[J]. SIAM Journal on Optimization, 1998, 8(3): 631-657.
[17]	SHARMA D, SHUKLA P K. Line-prioritized environmental selection and normalization scheme for many-objective optimization using reference-lines-based framework[J]. Swarm and Evolutionary Computation, 2019, 51: 100592.
[18]	张春伟，刘海江，姜冬冬. 基于遗传算法的白车身机器人焊接路径规划[J]. 同济大学学报(自然科学版), 2011, 39(4): 576-580.
	ZHANG Chunwei, LIU Haijiang, JIANG Dongdong. Robot welding route planning in car-body welding process based on genetic algorithm[J]. Journal of Tongji University (Natural Science), 2011, 39(4): 576-580.
[19]	ZITZLER E, THIELE L, LAUMANNS M, et al. Performance assessment of multiobjective optimizers: An analysis and review[J]. IEEE Transactions on Evolutionary Computation, 2003, 7(2): 117-132.
[20]	中华人民共和国住房和城乡建设部. 钢结构设计标准： GB 50017-2017[S]. 北京：中国建筑工业出版社，2017: 200.
	Ministry of Housing and Urban-Rural Development of the People’s Republic of China. Standard for design of steel structures: GB 50017-2017[S]. Beijing: China Architecture Publishing & Media Co., Ltd., 2017: 200.

杆件	型材	规格
上辅梁	H钢(h×B×t₁×t₂×r)	200×200×8×12×16
吊杆	无缝钢管(Φ×t₃)	80×6
平台层梁	H钢(h×B×t₁×t₂×r)	175×175×7.5×11×13

组别	m/t	σ_max/MPa	d_max/mm
优化前	40.996	9.96	－0.40
NSGA-III	28.624	19.47	－4.66
mNSGA-III	28.681	18.84	－1.92