A novel hybrid assembly method to reduce operational costs of selective assembly

Abstract

Selective assembly is a technique whereby components are smartly matched depending on their measured feature values. Although it can reduce scrap by allowing more relaxed tolerances on components when compared to traditional random assembly, it is often not adopted due to the high related operational costs. Therefore, this study proposes a novel hybrid strategy whereby a number of traditional assembly rounds are performed before switching to selective assembly. At each round, the components which did not lead to a feasible assembly are put into their respective unmatched piles and are used for the next traditional assembly round. When N such rounds have been performed, the remaining unmatched components will finally be assembled via selective assembly.To determine the optimal number of traditional assembly rounds before moving on to selective assembly, a cost function depending on the matching probabilities at each round and the relative costs of selective and traditional assembly is developed together with an optimality condition. Since the analytic calculation of the matching probabilities is in most of the practical cases infeasible, these are estimated using simulation. Particular attention is paid to reducing the variance of the estimators for the matching probabilities, and to their impact on the total variability of the cost function. The application of this innovative method to two case studies demonstrates that the proposed hybrid technique can significantly reduce operational costs of selective assembly, while still taking advantage of its typical benefits.