Assuring the Matchable Degree in Selective Assembly via a Predictive Model Based on Set Theory and Probability Method

Abstract

The major advantage of using selective assembly in manufacturing is that it allows the use of low precision parts to achieve high precision assembly. However, two problems arise: (a) the surplus parts due to unbalanced mating parts in some selective groups (b) there is no quantitative method to predict the matchable degree before assembly, and correspondingly no quantitative criterion on how to adjust manufacturing processes so that the desired match between mating parts could be guaranteed. By adopting the concepts of intersection and union from set theory and incorporating with the probability method, this paper develops a predictive model for quantitatively estimating the matchable degree between mating parts for selective assembly. Based on such a quantitative reference, together with the criterion for guaranteeing process capability index Cpk, an optimal algorithm for adjusting biases of dimensional distributions can be achieved to assure the matchable degree, thus improving the effectiveness of selective assembly.