摘要：Assembly dimensional quality affects wind noise and driving steady and whole external appearance. The quality can be improved by reducing part tolerance and fixture tolerance and optimizing key control characteristics (KCCs). However, reducing tolerance should largely increase manufacturing costs, and then the paper assembly tolerance is decreased by selecting optimal KCCs. In this work, a fitness function is presented to evaluate assembly operations based on the linear assembly variation analysis model. Afterwards, a new social radiation algorithm (SRA) is proposed to optimize KCCs, and some test functions are used to evaluate optimum performance between the genetic algorithm (GA) and SRA, and the results show that the performance of SRA is better than that of GA. Finally two cases are used to illustrate process of assembly operation optimization by SRA, and the results show that the SRA has higher precision and efficiency than that of GA.
