Multi-Object Selective Assembly Based on Hybrid Particle Swarm Algorithm

Considering the batch assembly with multiple function requirements (FR), the multi-object selective assembly (SA) technology based on the hybrid particle swarm algorithm is introduced in this paper. Firstly, the SA model is discussed, and a multi-object optimization problem is illustrated based on the assembly chains; additionally, the hybrid particle swarm algorithm is used to solve the multi-object selective assembly problem. The matching matrix of batch parts is designed as the particle. The local non-inferior particles and the global non-inferior particles of each iteration population are calculated. The particle population is update by crossover and mutation operations. After all the iterations, the final global non-inferior particles are selected as the matching strategy of batch parts. Based on the optimized matching strategy, the assembly success rate is calculated, and each assembly accuracy and its precision is guaranteed. The process of multi-object SA based on hybrid particle swarm algorithm is introduced, and an example of batch cabin sections are illustrated to obtain the optimized matching strategy of batch parts. Contrasted to the sequential strategy and the random strategy, it can find that the multi-object SA method based on hybrid particle swarm algorithm has advantage in guaranteeing the assembly success rate, and comprehensively decreasing each assembly accuracy.