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Selective Assembly for Components with Multiple Characteristics

Considering the limitation and production costs, the selective assembly is often used to enhance the precision of assembly of complex products. This paper presents an imporved grouping method based on MOPSO( Multi-Objective Particle Swarm Optimization) for selective assembly with multiple characteristics. The properties and quality criteria of selective assembly of components with multi-quality characteristics were analyzed to establish a mathematical model. The objective function of this model is to minimize the clearance variation in selective assembly. Fitness sharing strategy and dynamic archiving strategy are introduced to improve the solving performance of MOPSO. The method is applied to the optimization in a piston-cylinder selective assembly problem. The results show that the presented method can effectively reduce the clearance variation of of the product assembly