Modified Data Mining Approach for Defective Diagnosis in Hard Disk Drive Industry
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%.
[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.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:53286", author = "S. Soommat and S. Patamatamkul and T. Prempridi and M. Sritulyachot and P. Ineure and S. Yimman", title = "Modified Data Mining Approach for Defective Diagnosis in Hard Disk Drive Industry", abstract = "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.", volume = "3", number = "12", pages = "1477-6", }