摘要
传统轨道板运输车的设计过于保守,车架结构强度有较大余量,为减轻结构自重,减少制造成本,提出一种新的优化方法。该方法采用有限元软件ANSYS和优化软件ISIGHT,首先通过拉丁超立方设计方法选出对约束条件和目标函数影响较大的设计变量,然后根据选出的设计变量建立神经网络径向基近似模型,最后利用多岛遗传算法对近似模型进行优化。优化结果表明,该方法能提高优化效率,优化后轨道板运输车结构自重降低了9.9%,对轨道板运输车的设计有重要参考价值。
Traditional design methods of rail plate carrying vehicle tend to be conservative. The structural strength had a large sur- plus. To reduce weight and the manufacturing cost of the structure, a new structure optimization method was proposed. This meth- od combines the finite element software ANSYS and the optimization software ISIGHT. First of all, choosing the design variables which have large influence on constraint condition and objective function by the method of Latin Hypercube Design（LHD）. Then, the model of the neural network radial basis approximation is set up. Finally, the approximate model is optimized by using the Multi Island Genetic Algorithm（MIGA）. The results show that the method can greatly improve the efficiency of optimization, and the structure weight of rail plate carrying vehicle reduce 9.9 % by the optimization design which is of important significance for the design of rail plate carrying vehicle.
作者
肖泽平
于兰峰
邓勇
徐江平
Xiao Zeping;Yu Lanfeng;Deng Yong;Xu Jiangping(School of Mechanical Engineering,Southwest Jiaotong University,C hengdu 610031,China)
出处
《现代制造工程》
CSCD
北大核心
2018年第8期48-52,共5页
Modern Manufacturing Engineering
基金
国家自然科学基金项目(51675450)
中央高校基本科研业务费专项资金资助项目(2682016CX031)
关键词
轨道板运输车
优化设计
径向基函数神经网络
多岛遗传算法
rail plate carrying vehicle
optimization design
Radial Basis Function （RBF） neural network
Multi Island Genetic Al-gorithm （MIGA）
