Ponnaiah Peter Anand Kumar, Rathinam Maheswaran, and Mathana Singh Victor Raj


Complex assembly, precision, particle swarm optimization algorithm, selective assembly


An assembly consists of two or more mating parts. The quality of the assembly is mainly based on the quality of mating parts. The mating parts may be manufactured using different machines and processes with different standard deviations. Therefore, the dimensional distributions of the mating parts are not similar. This results in clearance between the mating parts. To obtain high precision assemblies, clearance variation has to be reduced. Selective assembly helps to reduce this clearance variation. Selective assembly is a method of obtaining high precision assembly from relatively low precision components. In selective assembly, the mating parts are manufactured with wide tolerances. The mating part population is then partitioned to form selective groups, and the appropriate selective groups are assembled interchangeably. This paper presents a modified particle swarm optimization (MPSO) to improve the precision of a complex assembly which consists of piston, piston ring and cylinder. The proposed MPSO is based on the concept that an individual in the PSO not only learns from its best experience, but also learns from its mistakes referred as personal worst position. The idea of this modification is based on the social behaviour that each particle tries to leave its previous worst position. The main distinction of this approach is in using particle’s worst (but worth) experience instead of the best previous experience. The results show that the proposed approach is more apt to find the global optima of the problem.

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