Roundness Measurement Using the PSO Algorithm
Measuring the roundness of a circular workpiece is a crucial issue of quality control and inspection in industry. 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 non-linear constrained optimization problems, have not been thoroughly discussed lately. This study proposes a 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 methods are 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 is analyzed. The proposed method is verified with a set of testing images and benchmarked with the GA-based (Genetic Algorithm) method (Chen, 2000). 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 result, several industrial applications are presented to explore the effectiveness and efficiency of the proposed method.
Roundness Measurement Machine Vision Particle Swarm Optimization Algorithm.
Te-Hsiu Sun Chun-Yuan Cheng Fang-Chih Tien
Dept. of Industrial Engineering and Management Chaoyang University of Technology Taichung, Taiwan Dept. of Industrial Engineering and Management National Taipei University of Technology Taipei, Taiw
国际会议
2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)
上海
英文
1228-1232
2011-07-26（万方平台首次上网日期，不代表论文的发表时间）