Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/50795
Title: Machine scoring model using data mining techniques
Authors: Wimalin S. Laosiritaworn
Pongsak Holimchayachotikul
Keywords: Engineering
Issue Date: 21-Oct-2010
Abstract: This article proposed a methodology for computer numerical control (CNC) machine scoring. The case study company is a manufacturer of hard disk drive parts in Thailand. In this company, sample of parts manufactured from CNC machine are usually taken randomly for quality inspection. These inspection data were used to make a decision to shut down the machine if it has tendency to produce parts that are out of specification. Large amount of data are produced in this process and data mining could be very useful technique in analyzing them. In this research, data mining techniques were used to construct a machine scoring model called 'machine priority assessment model (MPAM)'. This model helps to ensure that the machine with higher risk of producing defective parts be inspected before those with lower risk. If the defective prone machine is identified sooner, defective part and rework could be reduced hence improving the overall productivity. The results showed that the proposed method can be successfully implemented and approximately 351,000 baht of opportunity cost could have saved in the case study company.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=78651576439&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/50795
ISSN: 20103778
2010376X
Appears in Collections:CMUL: Journal Articles

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