Quality control problem in printed circuit board manufacturing - An extended rough set theory approach

Journal of Manufacturing Systems

Volumn 23, Issue 1, 2004, Pages 56-72

  Tseng, Tzu Liang ^a   Jothishankar, M C ^b   Wu, Tong ^c  

^a UNIVERSITY OF TEXAS AT EL PASO   (United States)

^b ROCKWELL COLLINS   (United States)

^c Arizona State University   (United States)

Author keywords

Data Mining; Manufacturing Fault Detection; PCB Assembly; Quality Engineering; Rough Set Theory

Indexed keywords

DATA MINING; DATA WAREHOUSES; KNOWLEDGE BASED SYSTEMS; PROBLEM SOLVING; QUALITY CONTROL; ROUGH SET THEORY;

FAULT DETECTION; MANUFACTURING FAULT DETECTION; PCB ASSEMBLY; QUALITY ENGINEERING;

PRINTED CIRCUIT BOARDS;

EID: 15744365241     PISSN: 02786125     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0278-6125(04)80007-4     Document Type: Article

References (32)

1. Altman E.I. Corporate Financial Distress and Bankruptcy 1993 John Wiley New York
2. Altman E.I., Marco G., and Varetto F. Corporate distress diagnosis: comparisons using discriminant analysis and neural networks Journal of Banking and Finance v18 1994 505 529
3. Apte C., Weiss S., and Grout G. Predicting defects in disk drive manufacturing: a case study in high-dimensional classification Proc. of 9th Conf. on Artificial Intelligence in Applications 1993 212 218
4. Berry M., and Linoff G. Data Mining Techniques: For Marketing, Sales, and Customer Support 1997 John Wiley New York
5. Beynon M., Curry B., and Morgan P. Knowledge discovery in marketing: an approach through rough set theory Knowledge Discovery in Marketing 2001 915 935
6. Breiman L. Classification and Regression Trees 1984 Wadsworth
7. Available at Cabena P., Choi H.H., Kim I.S., Otsuka S., Reinschmidt J., and Saarenvirta G. Intelligent minider for data application guide 2000
8. Cheung D.W., Ng VT., Fu A.W., and Fu Y. Efficient mining of sssociation rules in distributed databases IEEE Trans. on Knowledge and Data Engg. v8 n6 1996 911 922
9. Dhar V., and Stein R. Seven Methods,for Transforming Corporate Data into Business Intelligence 1997 Prentice-Hall Upper Saddle River, NJ
10. Dimitras A.I., Slowinski R., Susmaga R., and Zopounidis C. Business failure prediction using rough sets European Journal of Operational Research v114 n2 1999 263 280
11. Dubois D., and Prade H. Rough fuzzy sets and fuzzy rough sets Int'l Journal of General Systems v17 1990 191 209
12. Duckham M., Mason K., Stell J., and Worboys M. A formal approach to imperfection in geographic information Computers, Environment and Urban Systems v25 n1 2001 89 103
13. Heckerman D., Mannila H., Pregibon D., and Uthurusamy R. Proc. of 3rd Int'l Conf. on Knowledge Discovery and Data Mining 1997 AAAI Press Menlo Park, CA
14. Khoo L.-P., and Zhai L.-Y. A prototype genetic-algorithm-enhanced rough set-based rule induction system Computers in Industry v46 2001 95 106
15. Komorowski J., and Zytkow J. Principles of Data Mining and Knowledge Discovery 1997 Springer-Verlag New York
16. Kusiak A. Rough set theory: a data mining tool for semiconductor manufacturing IEEE Trans. on Electronics Packaging Mfg. v24 n1 2001 44 50
17. Kusiak A., Kern J.A., Kernstine K.H., and Tseng T.-L. Autonomous decision-making: a data mining approach IEEE Trans. on Information Technology in Biomedicine v4 n4 2000 274 284
18. Kusiak A., and Kurasek C. Data mining of printed circuit board defects IEEE Trans. on Robotics and Automation v17 n2 2001 191 196
19. Kusiak A., and Tseng T.-L. Modeling approach to data mining Proc. of Industrial Engg. and Production Mgmt. Conf. Glasgow, Scotland, July 1-13, 1999 1999
20. Available at Meng L.Q. Cognitive modeling of cartographic generalization 1998 inst/forskning/sb/ligiumeng.htm
21. (Spring) Ohlson J.A. Financial ratios and the probabilistic prediction of bankruptcy Journal of Accounting Research 1980 109 131
22. Pawlak Z. Rough sets Int'l Journal of Computer and Information Sciences v11 1982 341 356
23. Pawlak Z. Rough Sets 1991 Kluwer Academic Publishers Dordrecht, The Netherlands
24. Ruhe G. Qualitative analysis of software engineering data using rough sets Proc. of 4th Int'l Workshop on Rough Sets, Fuzzy Sets, and Machine Discovery Tokyo, 1996 292 299
25. Saxena S. Fault isolation during semiconductor manufacturing using automated discovery from wafer tracking databases Proc. of Knowledge Discovery in Databases Workshop 1993 81 88
26. Shen L., Tay F.E.H., Qu L., and Shen Y. Fault diagnosis using rough sets theory Computers in Industry v43 n1 2000 61 72
27. Slowinski R. Intelligent Decision Support Handbook of Applications and Advances of Rough Sets Theory 1992 Kluwer Academic Publishers Dordrecht, The Netherlands
28. Tseng T.-L. Quantitative approaches for information modeling PhD dissertation 1999 Univ. of Iowa Iowa City, IA
29. Vermeulen E.M., Spronk J., and Van der Wijst N. The application of the multi-factor model in the analysis of corporate failure C. Zopounidis Operational Tools in the Management of Financial Risks 1998 Kluwer Academic Publishers Dordrecht, The Netherlands 59 73
30. Wojcik Z.M. Rough sets for intelligent image filtering Proc. of Int'l Workshop on Rough Sets and Knowledge Discovery Banff, Alberta, Canada 1993 399 410
31. Woolery L., Grzymala-Busse J., Summers S., and Budihardjo A. The use of machine learning program LERS-LB 2.5 in knowledge acquisition for expert system development in nursing Computers in Nursing v9 n6 1991 227 234
32. Zhang J.B. Computer-aided visual inspection for integrated quality control Computers in Industry v30 1996 185 192