Minimization of clearance variation of a radial selective assembly using cohort intelligence algorithm

Abstract

Cohort intelligence is a socio-inspired self-organizing system that includes inherent, self-realized, and rational learning with self-control and ability to avoid obstacles (jumps out of ditches/local solutions), inherent ability to handle constraints, uncertainty by modular and scalable system and robust (immune to single point failure). In this method, a candidate self-supervises his/her behavior and adapts to the behavior of another better candidate, thus ultimately improving the behavior of the whole cohort. Selective assembly is a cost-effective approach to attaining necessary clearance variation in the resultant assembled product from the low precision elements. In this paper, the above-mentioned approach is applied to a problem of hole and shaft assemblies where the objective is to minimize the clearance variation and computational time. The algorithm was coded and run in MATLAB R2016b environment, and we were able to achieve convergence in less number of iterations and computational time compared to the other algorithms previously used to solve this problem.