Machine Scoring Model Using Data Mining Techniques
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.
[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
[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
@article{"International Journal of Information, Control and Computer Sciences:57871", author = "Wimalin S. Laosiritaworn and Pongsak Holimchayachotikul", title = "Machine Scoring Model Using Data Mining Techniques", 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.", volume = "4", number = "4", pages = "756-5", }