基于多目标优化的乘用车后排座椅轻量化设计

提出第二代非劣排序遗传算法(NSGA-II)结合响应面法(RSM)-径向基神经网络方法(RBF)混合近似模型和逼近理想解排序(TOPSIS)方法对某乘用车后排座椅进行结构-材料一体化多目标轻量化设计研究。结合有限元理论建立仿真模型,并通过行李箱碰撞试验验证仿真模型的正确性,根据工程经验和座椅靠背骨架吸能分析确定了6个优化部件厚度、材料的设计变量及取值范围;采用RSM-RBF混合近似模型方法拟合设计变量与响应之间的关系;利用NSGA-Ⅱ算法对优化问题进行求解,得到Pareto最优解集。最后采用基于熵权TOPSIS方法对Pareto最优解集进行排序确定最佳折中解。结果表明:在满足各项安全性能法规的前提下,乘用车后排座椅减重3.57 kg。

Lightweight Design of a Passenger Car Rear Seat Based on Multi-objective Optimization

A method combining the non-dominated sorting genetic algorithm(NSGA-II),the response surface method(RSM)-radial basis function neutral network(RBF)hybrid approximation model and the technique for ordering preferences by similarity to ideal solution(TOPSIS)was proposed,used for structure-material integrated multi-objective lightweight design of the rear seat of a passenger car.The simulation model was established based on the finite element theory,and the correctness of the simulation model was verified by the experiments of luggage crash.Based on engineering experience and energy absorption analysis of seat rear frame,the design variables and value ranges of the six optimized components'thickness and materials were determined.The RSM-RBF hybrid approximation model was used to fit the relationship between design variables and responses.The NSGA-II algorithm was used to solve the optimization problem and obtain the Pareto optimal solution set.Finally,the TOPSIS method based on entropy weight was adopted to sort the optimal solution set of Pareto to determine the optimal compromise solution.The results indicate that the total mass of the passenger car rear seat is reduced by 3.57 kg,while under the premise of meeting the safety performance regulations.