Abstract
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.
Highlights
The quality of autobody dimension is related to the whole external appearance, wind noise, and even the steady of driving
Dimension quality can be improved through selecting assembly sequence, designing fixture layouts, and optimizing key control characteristics (KCCs)
According to the analysis of assembly operations, assembly tolerance changes due to different assembly operations, which change with different assembly sequences
Summary
The quality of autobody dimension is related to the whole external appearance, wind noise, and even the steady of driving. The dimension quality of autobody is mainly influenced by automobile parts design, assembly process, and manufacturing variations [1]. Dimension quality can be improved through selecting assembly sequence, designing fixture layouts, and optimizing key control characteristics (KCCs). Since the current optimization algorithms are not able to converge to the global optimal solutions, this paper proposes a new social radiation algorithm (SRA) to improve optimization precision and efficiency for increasing the probability of searching global solutions. This method is proposed based on human competition in society. The innovation points of SRA are that the population is divided into different subgroups and resorted at every generation
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