Applying particle swarm optimization algorithm to roundness measurement

Abstract

Measuring the roundness of a circular workpiece is a common problem of quality control and inspection. In this area, maximum inscribed circle (MIC) and maximum circumscribing circle (MCC), minimum zone circle (MZC) and least square circle (LSC) are four commonly used methods. In particular, MIC, MCC, and MZC, which are nonlinear constrained optimization problems, have not been thoroughly discussed lately. This study proposes a machine vision-based roundness measuring method that applies the particle swarm optimization algorithm (PSO) to compute MIC, MCC and MZC. To facilitate the PSO process, five different PSO’s were encoded using a radius ( R) and circle center ( x, y) and extensively evaluated using an experimental design, in which the impact of inertia weight, maximum velocity and the number of particles on the performance of the particle swarm optimizer was analyzed. The proposed method was verified with a set of testing images and benchmarked with the GA-based (genetic algorithm) method [Chen, M. C. (2000). Roundness inspection strategies for machine visions using non-linear programs and genetic algorithms. International Journal of Production Research, 38, 2967–2988]. The experimental results reveal that the PSO-based method effectively solved the MIC, MCC, and MZC problems and outperforms GA-based method in both accuracy and the efficiency. As a finals, several industrial applications are presented to explore the effectiveness and efficiency of the proposed method.