Data Mining to Recognize Fail Parts in Manufacturing Process

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Published: May 27, 2008
DOI: https://doi.org/10.37936/ecti-eec.200972.171876
Keywords:
Fault Diagnosis Association Rule Learning Data Mining Hard Disk Drive Process Manufacturing

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Wanida Kanarkard
Department of Computer Engineering, Khon Kaen University
Danaipong Chetchotsak
Department of Industrial Engineering, Khon Kaen University
Daranee Hormdee
Department of Computer Engineering, Khon Kaen University
Rod Adams
Department of Computer Science, University of Hertfordshire
Neil Davey
Department of Computer Science, University of Hertfordshire

Abstract

In many manufacturing processes, some key process parameters have very strong relationship with the normal or various faulty products of finished products. The abnormal changes of these process parameters could result in various categories of faulty products. In this paper, a data mining model is developed for on-line intelligent monitoring and diagnosis of the manufacturing processes. In the proposed model, an Apriori learning rules developed for monitoring the manufacturing process and recognizing faulty quality of the products being produced. In addition, this algorithm is developed to discover the causal relationship between manufacturing parameters and product quality. These extracted rules are applied for diagnosis of the manufacturing process; provide guidelines on improving the product quality. Therefore, the data mining system provides abnormal warnings, reveals assignable cause(s), and helps operators optimally set the process parameters. The proposed model is successfully applied to an assembly line in hard disk drive process, which improves the product quality and saves manufacturing cost.

Article Details

How to Cite
Kanarkard, W., Chetchotsak, D., Hormdee, D., Adams, R., & Davey, N. (2008). Data Mining to Recognize Fail Parts in Manufacturing Process. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 7(2), 33–38. https://doi.org/10.37936/ecti-eec.200972.171876
Issue
Vol. 7 No. 2 (2009): Regular Issue
Section
Research Article

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