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基于稳健性多目标优化的微客尾门轻量化设计
引用本文:袁廷辉,倪士林,王霄,刘会霞.基于稳健性多目标优化的微客尾门轻量化设计[J].机电工程,2017,34(8):860-864.
作者姓名:袁廷辉  倪士林  王霄  刘会霞
作者单位:江苏大学 机械工程学院,江苏 镇江,212013
摘    要:针对微客尾门结构的轻量化设计问题,提出了一种基于稳健性多目标优化的尾门轻量化设计方法。首先通过灵敏度分析方法筛选出了对尾门性能贡献量较大的尾门零件厚度作为设计变量,并建立了尾门各个工况响应的近似模型,其次以尾门扭转刚度、下垂刚度、尾门前三阶模态频率和抗凹分析点为约束条件,以尾门质量最小和第一阶模态频率最大和弯曲刚度位移量最小为目标,利用NSGA-Ⅱ算法对尾门进行了多目标优化,通过蒙特卡洛模拟技术对尾门进行了6σ质量水平和可靠度分析,并进行了6σ稳健性多目标优化。最终结果表明:优化设计后尾门结构在满足各性能要求的情况下实现了轻量化,质量减轻了2.28 kg,且弯曲刚度和第一阶模态得到提高;同时尾门结构各工况性能的稳健性也得到改善,达到6σ质量水平。

关 键 词:尾门  轻量化  多目标优化  6σ稳健性

Tail gate lightweight of mini bus based on robustness and multi-objective optimization
YUAN Ting-hui,NI Shi-lin,WANG Xiao,LIU Hui-xia.Tail gate lightweight of mini bus based on robustness and multi-objective optimization[J].Mechanical & Electrical Engineering Magazine,2017,34(8):860-864.
Authors:YUAN Ting-hui  NI Shi-lin  WANG Xiao  LIU Hui-xia
Abstract:Aiming at the problem of lightweight design for the tail gate structure on mini bus, a method based on 6σrobustness analysis and multi-objective optimization was presented. Firstly, the sensitivity analysis was used to pick out tail gate parts contributing more to tail gate performance as the design variables, and then the approximate models of each working condition response of tail gate were established. Sec-ondly, taking tail gate torsion stiffness, drooping stiffness, the first three modal frequency, and dent resistance as the constrains, taking the minimum mass, maximum first modal frequency and minimum bending stiffness displacement of tail gate as targets, the multi-objective opti-mization was conducted by using the NSGA-Ⅱ, then the 6σquality level analysis and robustness multi-objective optimization were carried out by using Monte Carlo simulation. The results indicate that under the requirements of all performance, the mass of tail gate is reduced by 2. 28 kg and the bending stiffness and first modal frequency are improved. At the same time, the robustness of each working condition perform-ance of the tail gate structure is also improved,reaching 6σ quality level.
Keywords:tail gate  lightweight  multi-objective optimization  6σ robustness
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