首页|铸铝一体化车门的多目标可靠性优化设计

铸铝一体化车门的多目标可靠性优化设计

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  • 国家科技期刊平台
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  • NSTL
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为提升车门的轻量化水平与性能,采用"材料—结构—性能"一体化集成方法设计铸铝一体化车门.基于构建的铸铝一体化车门有限元模型,以车门的厚度为设计变量,采用径向基函数(radial basis function,RBF)神经网络近似模型和二阶响应面近似模型并分别结合二代非支配排序遗传算法(non-dominated sorting genetic algorithm-Ⅱ,NSGA-Ⅱ)、多目标粒子群优化(multi-objective particle swarm optimization,MOPSO)算法以及多岛遗传算法(multi-island genetic algorithm,MIGA)对车门的下沉刚度工况位移、上扭转刚度工况位移、下扭转刚度工况位移、一阶弯曲模态频率、一阶扭转模态频率和质量进行确定性优化设计.在此基础上,考虑材料及加工制造等不确定性因素,对确定性优化解的质量水平进行6Sigma可靠性分析与优化.结果表明,二阶响应面近似模型与MOPSO算法的优化组合方案实现了车门的最佳轻量化,RBF神经网络近似模型与MOPSO算法的优化组合方案实现了车门下沉刚度工况位移的最小化.上述2种组合分别实现了车门轻量化与安全化的设计目标.研究结果可为车身零部件的优化设计提供参考.
Multi-objective reliability optimization design for cast aluminum integrated car door
In order to improve the lightweight level and performance of the car door,the integrated method of"material-structure-performance"is adopted to design the cast aluminum integrated car door.Based on the constructed finite element model of the cast aluminum integrated car door,with the thickness of the car door as the design variable,the radial basis function(RBF)neural network approximation model and the second-order response surface approximation model were used in combination with the non-dominated sorting genetic algorithm-Ⅱ(NSGA-Ⅱ),multi-objective particle swarm optimization(MOPSO)algorithm and multi-island genetic algorithm(MIGA)to conduct the deterministic optimization design for the sinking stiffness condition displacement,upper torsional stiffness condition displacement,lower torsional stiffness condition displacement,first-order bending mode frequency,first-order torsional mode frequency and mass of the car door.On this basis,the 6Sigma reliability analysis and optimization for the quality level of the deterministic optimization solution were carried out considering the uncertainties of materials and manufacturing.The results showed the optimal combination of second-order response surface approximation model and MOPSO algorithm achieved the optimal lightweight of the car door,and the optimal combination of RBF neural network approximation model and MOPSO algorithm could minimize the the displacement of the car door under the sinking stiffness condition.The above two combinations achieved the design goals of lightweight and safety for the car door,respectively.The research results can provide reference for the optimization design of car parts.

cast aluminum integrated car doorlightweightradial basis function neural network approximation modelsecond-order response surface approximation modelmulti-objective particle swarm optimization algorithm6Sigma reliability

吴勃夫、吴姚烨、贝璟、吴宗扬、孙亮

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合肥工业大学 汽车与交通工程学院,安徽 合肥 230009

铸铝一体化车门 轻量化 径向基函数神经网络近似模型 二阶响应面近似模型 多目标粒子群优化算法 6Sigma可靠性

国家自然科学基金资助项目

51875150

2024

10.3785/j.issn.1006-754X.2024.03.159

工程设计学报
浙江大学 中国机械工程学会

工程设计学报

CSTPCD北大核心
影响因子：0.694
ISSN：1006-754X
年,卷(期)：2024.31(2)
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