A novel equal area-equal width-equal bin numbers technique using salp swarm optimization algorithm for maximizing the success rate of ball bearing assembly

Abstract

In this work, an algorithmic technique is used to minimize the excess parts and maximize the success rate of selective assembly. In this study, a unique method known as Equal Area-Equal Width-Equal Bin Numbers is introduced to group the parts of a ball bearing assembly by taking into account their range of tolerance. A full factorial design is used to conduct the experiments, and the salp swarm optimization (SSO) algorithm is employed to evaluate the best bin combinations and identify the possibility of making the maximum number of assemblies. Computational results showed a 13.16 percent increase in success rate when compared to prior research when employing the proposed method. Comparing the computational outcomes versus those obtained by the Antlion optimization and Genetic algorithms validates the adoption of the SSO algorithm. A paired T-test is performed to assess the statistical significance of the findings. The convergence plot further supports the superiority of the SSO algorithm.