Artificial Intelligence Approaches for The Evaluation of Form Error

• Main Authors: Sheng-Kai Kang, 康勝凱
• Other Authors: Yi-Chih Hsieh
• Format: Others
• Language: zh-TW
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/85vu4y

碩士 === 國立虎尾科技大學 === 工業工程與管理研究所 === 100 === Form error is important to to the quality of piece parts. The rotation parts are the most widely used components in industrial production, and the form error is an important indicator. As known, it is also the key to the product quality. There are various approaches to evaluate the form errors for objects. In this thesis, we apply artificial intelligence approaches to evaluate different form errors, including roundness error, cube error, cylindricity error and conicity error. In this thesis, we apply three heuristic algorithms for solving the form error problem, including particle swarm optimization, immune algorithm and genetic algorithm. In addition, in order to ensure that the solution quality, in this study, we use the statistical test method to compare the results of three heuristic algorithms. In this thesis, we experiment four types of test problems for roundness error, cube error, cylindricity error and conicity error. Numerical results show that the solutions by the immune algorithm are better than those by particle swarm optimization and genetic algorithm, respectively.