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Modified Data Mining Approach for Defective Diagnosis in Hard Disk Drive Industry

Authors: S. Soommat, S. Patamatamkul, T. Prempridi, M. Sritulyachot, P. Ineure, S. Yimman


Currently, slider process of Hard Disk Drive Industry become more complex, defective diagnosis for yield improvement becomes more complicated and time-consumed. Manufacturing data analysis with data mining approach is widely used for solving that problem. The existing mining approach from combining of the KMean clustering, the machine oriented Kruskal-Wallis test and the multivariate chart were applied for defective diagnosis but it is still be a semiautomatic diagnosis system. This article aims to modify an algorithm to support an automatic decision for the existing approach. Based on the research framework, the new approach can do an automatic diagnosis and help engineer to find out the defective factors faster than the existing approach about 50%.

Keywords: Slider process, Defective diagnosis and Data mining

Digital Object Identifier (DOI):

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[1] A.Harding, M. Shahbaz, Srinivas and A. Kusiak, "Data mining in manufacturing: A review," J. Manufacturing Science and Engineering, vol. 128, Nov. 2006, pp.969-976.
[2] C. Chen-Fu, W. Wen-Chih and C. Jen-Chieh, "Data mining for yield enhancement in semiconductor manufacturing and an empirical study," ELSEVIER, J. Expert system with application, vol. 33, 2007, pp. 192-198.
[3] S. Soommat, S. Patamatamkul, M. Sritulyachot, P..Ineure and S. Yimman, "Applying data mining approach for slider yiled diagnosis in HDD manufacturing," presented at the IQC2008 Inter Conference Bangkok, Thailand , November 26-28, 2008., Paper C10.
[4] S. Soommat, S. Patamatamkul, M. Sritulyachot,P. .Ineure and S. Yimman, " Defective diagnosis using deciction trees and multivariate chart: A case study in hard disk dive industry," in Proc. 14st National Grauate Conf., KMUTNB, Bangkok, Thaiand , 2009, pp. 25-35.
[5] C. Wei-Chou, T. Shian-Shyong and W. Ching-Yao, "A novel manufacturing defect detection method using association rule mining techniques," ELSEVIER J. Expert system with application, vol. 29, 2005, pp. 807-815.
[6] J. Mecqueen, "Some methods for classification and analysis of multivarate observations," in proc. 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967, pp.281-297.
[7] D.C.Montgomery and G C. Runger, Applied Statistcs and Probability for Engineers. 2nd ed., USA., New York: Addison-Wesley, 1999, ch.9 and ch.14.
[8] SAS Institute Inc, JMP Statistics and Graphics guide. 5th ed., USA: SAS Institue Inc., 2002, ch.37.
[9] I.H.Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques. 2nd ed., Morgan Kaufmann Publis.USA, San Fan : Elsevier Inc., 2005, ch.3-ch.5.
[10] R.J. Roiger and M. W. Geatz. Data Mining: A Tutorial-Based Primer. Int. ed., USA., New York : Addison-Wesley, 2003., ch.1-ch.13.