Reducing Clearance Variation in Smart Matching Components by Using Genetic Algorithm

Quality is the most important aspect of the product for its survival in the market. Quality of the product depends on the quality of its components and their assembly. The mating parts may be manufactured by using different machines and processes which results in dissimilar dimensional distribution of the mating parts. Interchangeable assembly has long been known as the fundamental principle of assembly process in which the parts are assembled by randomly picking them from the lot of components with dimensions varying in predefined tolerances. But this approach fails to achieve higher precision with closer clearance variation. Selective assembly gives closer clearance variation, but there are often surplus parts due to undesirable dimensional distribution of mating parts. In this work, a smart matching of components is proposed in order to achieve closer clearance variation. The Genetic algorithm with sorting operator is coded with MATLAB and designed to obtain the smart matching of components. This genetic algorithm is applied separately for each pair of components in the assembly. The proposed method is tested and found to be more efficient than other existing method (Random matching) in the literature.