A novel manufacturing defect detection method using association rule mining techniques

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...

Full description

Saved in:
Bibliographic Details
Published inExpert systems with applications Vol. 29; no. 4; pp. 807 - 815
Main Authors Chen, Wei-Chou, Tseng, Shian-Shyong, Wang, Ching-Yao
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.11.2005
Subjects
Association rule mining
Defect detection
Interestingness measurement
Manufacturing defect detection problem
Association rule mining
Interestingness measurement
Manufacturing defect detection problem
Defect detection
Online AccessGet full text

Cover

More Information
Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2005.06.004