A novel manufacturing defect detection method using association rule mining techniques

Authors:
Wei-Chou Chen;Shian-Shyong Tseng;Ching-Yao Wang
Affiliations:
Department of Computer and Information Science, National Chiao Tung University, Hsinchu 300, Taiwan, ROC;Department of Computer and Information Science, National Chiao Tung University, Hsinchu 300, Taiwan, ROC;Department of Computer and Information Science, National Chiao Tung University, Hsinchu 300, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

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Abstract

In recent years, manufacturing processes have become more and more complex, and meeting high-yield target expectations and quickly identifying root-cause machinesets, the most likely sources of defective products, also become essential issues. In this paper, we first define the root-cause machineset identification problem of analyzing correlations between combinations of machines and the defective products. We then propose the Root-cause Machine Identifier (RMI) method using the technique of association rule mining to solve the problem efficiently and effectively. The experimental results of real datasets show that the actual root-cause machinesets are almost ranked in the top 10 by the proposed RMI method.