Manufacturing Defect Detection using Data Mining Approach

• Main Authors: Yu-Lin Kuo, 郭毓麟
• Other Authors: Shian-Shyong Tseng
• Format: Others
• Language: en_US
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/39119209999990598665

碩士 === 國立交通大學 === 資訊科學系 === 91 === In recent years, the procedure of manufacturing has become more and more complex. In order to meet high expectation on quality target, quick identification of root cause that makes defects is an essential issue. Traditional statistic-based methods are still difficult to identify the root cause due to the resulting multi-factor & nonlinear interactions or intermittent problem. In this thesis, Manufacturing Defect Detection Problem is formally defined and a corresponding methodology, called Root cause Machineset Identifier (RMI), is also proposed. RMI has three procedures to handle such Manufacturing Defect Detection Problem. Finally, the results of experiment show the accuracy and efficiency of RMI are both well with real manufacturing cases.