摘要
硬岩掘进机优化设计过程中设计变量较多的问题,采用应力分布为设计变量,再通过反问题计算得到截割头来间接对截割头进行参数化;针对评价函数计算量太大的问题,根据试验设计理论安排训练样本,采用神经网络建立设计变量与目标函数间的复杂的响应关系,并且详细研究了径向基函数网络在对评价函数进行预测过程中的应用,建立了一种新的截割头优化设计方法。与传统的优化方法相比,其设计变量数目较少,利用此方法对硬岩掘进机截割头的能耗和效率进行优化,所得截割头破碎性能良好,从而验证了此方法的有效性。
The ordinary parameterization programs for cutting head of hard rock machine contain a great deal of design variables,so an indirect parameterization method was proposed,where the stress distribution was treated as design variable.The blade was calculated by inverse design method and neural networks was adopted to construct the response relationshie between the design variables and the objective function.The sample data used to train neural network was schemed according to the theory of experiment design,the application of radial basic function network was investigated in detail,and a new optimization method was proposed.Compared with the traditional optimization method,here the design variable number is few,the calculation time is shortened obviously;and in this mothed,the energy consumption and the efficiency was selected as the objective functions for optimization.Finaly the validity of this newly proposed method is confirmed.
出处
《机械设计与研究》
CSCD
北大核心
2010年第2期99-101,共3页
Machine Design And Research
基金
辽宁省安全生产科技发展指导性计划资助项目(2009)
中国煤炭工业科技计划资助项目(MTDJ2009-269)
工业装备结构分析国家重点实验室开放基金(GZ0818)
关键词
反问题
神经网络
优化设计
硬岩掘进机
inverse problem
neural network
optimization design
hard rock machine
