Applying artificial intelligent techniques to set manufacturing parameters automatically---using injection molding machines as an example

• Main Authors: Shih-Chang Chen, 陳世璋
• Other Authors: Tong-Hsu Hou
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/48750975151379072744

碩士 === 國立雲林科技大學 === 工業工程與管理研究所碩士班 === 93 === In the thesis, variable neighborhood tabu search (VNTS) and Decision Tree Algorithm are used to implement the rule induction system (RIS). VNTS with the well-trained BPN criterion demonstrates good searching capability to find the best solution for parameter setting of injection molding machine. Decision Tree Algorithm is the tool for inducting rules from the experimental data. Taguchi method is adopted to perform task of experiment design. The illustrated examples in the empirical study show how this RIS works and sets up rules for machines. The rules inducted by Decision Tree Algorithm will be revised according to the best solution found by VNTS. The results show not only are the rules found by RIS pretty close to what the literature did, but also can RIS provide both the best parameters of injection molding machines and the appropriate machine operation rules to work staff in the workspace.