An optimization method for metamorphic mechanisms based on multidisciplinary design optimization

作     者：Zhang Wuxiang Wu Teng Ding Xilun 

作者机构：State Key Laboratory of Robotics and System Harbin Institute of Technology Institute of Robotics Beihang University 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2014年第27卷第6期

页      面：1612-1618页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0804[工学-仪器科学与技术] 0802[工学-机械工程] 0825[工学-航空宇航科学与技术] 0801[工学-力学（可授工学、理学学位）] 

基　　金：the fundamental support of the National Natural Science Foundation of China (Nos. 51105013, 51125020) the Beijing Natural Science Foundation of China (No. 3133042) the fundamental support provided by the China Scholarship Council and the State Key Laboratory of Robotics and System (HIT) 

主　　题：Configuration Mechanism Metamorphic mechanisms Method Multidisciplinary design optimization Optimization model 

摘      要：The optimization of metamorphic mechanisms is different from that of the conventional mechanisms for its characteristics of multi-configuration. There exist complex coupled design variables and constraints in its multiple different configuration optimization models. To achieve the compatible optimized results of these coupled design variables, an optimization method for metamorphic mechanisms is developed in the paper based on the principle of multidisciplinary design optimization（MDO）. Firstly, the optimization characteristics of the metamorphic mechanism are summarized distinctly by proposing the classification of design variables and constraints as well as coupling interactions among its different configuration optimization models. Further, collaborative optimization technique which is used in MDO is adopted for achieving the overall optimization performance. The whole optimization process is then proposed by constructing a two-level hierarchical scheme with global optimizer and configuration optimizer loops. The method is demonstrated by optimizing a planar five-bar metamorphic mechanism which has two configurations,and results show that it can achieve coordinated optimization results for the same parameters in different configuration optimization models.