tool based on an improved CSSO

 

 

 

LIU Kai1, LUO Weilin1*, LAI Yuyang2, FANG Liqiao2

 

(1. College of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350116, China;

 

2. Beijing SOYOTEC Co. Ltd., Beijing 100062, China)

 

 

Abstract: Aiming at selecting optimal design variables of each sub discipline and then passed them to system level when it was optimizing in CSSO method, improving the optimization efficiency and convergence speed of system level, an improved concurrent subspace optimization (ICSSO) was proposed. An automatic screening process was added for the proposed method based on the traditional CSSO, that is, by comparing schemes from subdisciplines and system level, an optimal scheme was determined and taken as the initial scheme for next iteration, meanwhile Latin hypercube of DOE method was used to set up a database, an approximate model was constructed by using radial basis function neural Network. ICSSO was incorporated into Isight platform and applied to the design of a machine tool spindle. The results indicate that the efficiency of the proposed ICSSO gains over the conventional CSSO.

 

Key words: multidisciplinary design optimization(MDO); concurrent subspace optimization (CSSO); latin hypercube method; approximate model; design of machine tool spindle