DETERMINISTIC MULTI-OBJECTIVE LIGHTWEIGHT DESIGN OF C-BEAM BASED ON RESPONSE SURFACE AND GENETIC ALGORITHM

CHEN HaoRan; XIANG ZhongKe; CHENG WenMing; XIANG FeiFei

doi:10.16579/j.issn.1001.9669.2021.03.019

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DETERMINISTIC MULTI-OBJECTIVE LIGHTWEIGHT DESIGN OF C-BEAM BASED ON RESPONSE SURFACE AND GENETIC ALGORITHM

更新时间：2022-09-22

- DETERMINISTIC MULTI-OBJECTIVE LIGHTWEIGHT DESIGN OF C-BEAM BASED ON RESPONSE SURFACE AND GENETIC ALGORITHM
- Journal of Mechanical Strength Vol. 43, Issue 3, Pages: 636-642(2021)
- 作者机构：
  
  1. 江西农业大学工学院
  2. 江西科技学院协同创新中心
  3. 江西科技学院汽车工程学院
- 作者简介：
- 基金信息：
- DOI：10.16579/j.issn.1001.9669.2021.03.019
  CLC：
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CHEN HaoRan, XIANG ZhongKe, CHENG WenMing, et al. DETERMINISTIC MULTI-OBJECTIVE LIGHTWEIGHT DESIGN OF C-BEAM BASED ON RESPONSE SURFACE AND GENETIC ALGORITHM. [J]. 43(3):636-642(2021)
DOI：

CHEN HaoRan, XIANG ZhongKe, CHENG WenMing, et al. DETERMINISTIC MULTI-OBJECTIVE LIGHTWEIGHT DESIGN OF C-BEAM BASED ON RESPONSE SURFACE AND GENETIC ALGORITHM. [J]. 43(3):636-642(2021) DOI： 10.16579/j.issn.1001.9669.2021.03.019.

摘要

针对大客车空气悬架关键结构件C型梁的轻量化设计问题。首先,基于参数化计算系统建立起符合实际工况的有限元模型,并完成最大等效应力、弯曲刚度和疲劳寿命分析,然后,采用中心复合设计试验方法,并基于全二阶多项式对响应面模型拟合,根据灵敏度分析筛选设计变量。最后,以质量最小和弯曲刚度最大为目标函数,同时以最大等效应力和疲劳寿命50万次循环为约束条件,采用非支配排序多目标遗传算法对C型梁进行优化设计,得到Pareto非劣前沿,根据实际设计需要选取妥协解。结果表明:轻量化设计后的C型梁在不改变原材料且满足约束条件的前提下,弯曲刚度与优化前基本保持一致,实现减重2.68 kg,减重比达3.6%。

Abstract

The lightweight design of C-beam structure which is a key structure component of bus air suspension will be mainly discussed in this paper. Firstly,based on parameterized calculating system,the finite element model that is in line with the actual working condition was established. Meanwhile,the maximum equivalent stress,bending stiffness and fatigue life analysis were completed. Then the central composite design experimental method was used,and the response surface model（ RSM） was fitted based on full 2 nd order polynomials,and design variables were screened according to sensitivity analysis.Finally,take the minimum mass as well as maximum bending stiffness as the objective function. At the same time,the maximum equivalent stress and the fatigue life of 500,000 cycles are taken as the constraint conditions. The optimization was conducted by NSGA-II multi-objective genetic algorithm,and in series Pareto non-inferior front curve was obtained,and the compromising solution was selected according to the actual design needs. The results indicate that the bending stiffness of the lightweight cbeam basically stay the same as before without changing the original material and meeting the constraint conditions,and the weight loss is 2. 68 kg and the weight loss ratio is 3. 6%.

关键词

C型梁参数化RSM灵敏度分析多目标轻量化遗传算法

Keywords

C-beamParameterizationRSMSensitivity analysisMulti-objective optimizationGenetic algorithm

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