Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
Machine Scoring Model Using Data Mining Techniques

Authors: Wimalin S. Laosiritaworn, Pongsak Holimchayachotikul

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.

Keywords: Computer Numerical Control, Data Mining, HardDisk Drive.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1071566

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1093

References:


[1] Han, J., & Kamber, M., Data mining: concepts and techniques. Morgan Kaufmann Publishers., 2001
[2] Irani, K. B., Cheng, J., Fayyad, U. M., and Qian, Z., "Applying Machine Learning to Semiconductor Manufacturing," IEEE Expert, vol. 81, pp. 41-47. 1993.
[3] Lee, M. H., "Knowledge Based Factory," Artificial Intelligence in Engineering, vol. 8, pp.109-125, 1993.
[4] Fayyad, U., and Stolorz. P., "Data Mining and KDD: Promise and Challenges," Future Generation Computer System,vol.13, pp. 99-115. 1997.
[5] Gelbard, R., Goldman, O., and Spiegler, I., "Investigating Diversity of Clustering Methods: An empirical Comparison," Data & Knowledge Engineering.,vol. 63, pp. 155-166, 2007.
[6] Chiu, T., Fang, D., Chen, J., Wang, Y., Jeris. C., "A Robust and Scalable Clustering Algorithm for Mixed Type Attributes in Large Databases Environment." In: Proc. 7th ACM SIGSDD International conference on Knowledge Discovery and Data Mining, 2001, pp.263-268.
[7] Fishburn P.C. Method for estimating additive utilities, Management Science, vol.13-17, 1997, pp.435-453