Abstract
The large amount of bulky and noisy shop floor data is one of the characteristics of the process industry. These data should be effectively processed to extract working knowledge needed for the enhancement of productivity and the optimization of quality. The objective of the chapter is to present an intelligent process control system integrated with data mining architecture in order to improve quality. The proposed system is composed of three data mining modules performed in the shop floor in real time: preprocessing, modeling, and knowledge identification. To consider the relationship between multiple process variables and multiple quality variables, the Neural-Network/Partial Least Squares (NNPLS) modeling method is employed. For our case study, the proposed system is configured as three control applications: feedback control, feed-forward control, and in-process control, and then applied to the shadow mask manufacturing process. The experimental results show that the system identifies the main causes of quality faults and provides the optimized parameter adjustments.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Albus, J. S., “Outline for a theory of intelligence,” IEEE Transactions on Systems, Man, and Cybernetics, 21 (3), 473–509, 1991.
Arentsen, L., Tiemersma, J. J., and Kals, H. J. J., “The integration of quality control and shop floor control,” International Journal of Computer Integrated Manufacturing, 9 (2), 113–130, 1996.
Biegler, L., “Advances in nonlinear programming concepts for process control,” Journal of Production Control, 8 (5), 301–311, 1998.
Boulet, B., Chhabra, B., Harhalakis, G., Minis, L., and Proth, J. M., “Cell controllers: Analysis and comparison of three major projects,” Computers in Industry, 16 (3), 239–254, 1991.
Camacho, E., Model Predictive Control in the Process Industry, Springer Verlag: New York, 1995.
Cho, H. and Wysk, R. A., “Intelligent workstation controller for computer integrated manufacturing: Problems and models,” Journal of Manufacturing Systems, 14 (4), 252–263, 1995.
Cybenko, G., “Approximation by superposition of a sigmoidal function,” Mathematics of Control, Signals, and Systems, 2 (4), 303–314, 1989.
Davis, W. J., Jones, A. T. and Saleh, A., “Generic architecture for intelligent control systems,” Computer Integrated Manufacturing Systems, 5 (2), 105–113, 1992.
Escalante, E. J., “Quality and productivity improvement: A study of variation and defects in manufacturing,” Quality Engineering, 11 (3), 427–442, 1999.
Fayyad, U. and Stolorz, P., “Data mining and KDD: Promise and challenges,” Future Generation Computer Systems, 13, 99–115, 1997.
Geladi P. and Kowalski, B. R., “Partial least-squares regression: A tutorial,” Anlytica Chimica Acta, 185, 1–17, 1986.
Hocking, R. R., “The analysis and selection of variables in linear regression,” Biometrics, 32, 1–51, 1976.
Hornik, K., Stinchcombe, M., and White, H., “Multilayer feedforward neural networks are universal approximators,” Neural Networks, 2, 359–366, 1989.
Jones, A. T. and Saleh, A., “A multi-level/multi-layer architecture for intelligent shop floor control,” International Journal of Computer Integrated Manufacturing, 3, 60–70, 1990.
Koonce, D.A., Fang, C., and Tsai, S., “A data mining tool for learning from manufacturing systems,” Computers and Industrial Engineering, 33 (1–2), 27–30, 1997.
Lavington, S., Dewhurst, N., Wilkins, E., and Freitas, A., “Interfacing knowledge discovery algorithms to large database management systems,” Information and Software Technology, 41, 605–617, 1999.
Lin, D., “Spotlight interaction effects in main-effect plans: A supersaturated design approach,” Quality Engineering, 11 (1), 133–139, 1998.
Lingarkar, R., Liu, L., Elbestawi, M. A., and Sinha, N. K., “Knowledge-based adaptive computer control in manufacturing systems: A case study,” IEEE Transactions on Systems, Man, and Cybernetics, 20 (3), 606–618, 1990.
Maíthouse, E., Tamhane, A., and Mah, R., “Nonlinear partial least squares,” Computers and Chemical Engineering, 21 (8), 875–890, 1997.
Mitra, A., Fundamentals of Quality Control and Improvement, Macmillan: New York, 1993.
Montgomery, D. C., Design and Analysis of Experiments, 4th ed., Wiley: New York, 1997.
Myers, R. H. and Montgomery, D. C., Response Surface methodology: Process and Product Optimization Using Designed Experiments, Wiley: New York, 1995.
Okushima, I. and Hitomi, K., “A study of economy machining: An analysis of maximum-profit cutting speed,” International Journal of Production Research, 3, 73–84, 1964.
Qin S. J. and McAvoy, T. J., “Nonlinear PLS modeling using neural networks,” Computers and Chemical Engineering, 16 (4), 379–391, 1992.
Rangwala, S. S. and Dornfeld, D. A., “Learning and optimization of machining operations using computing abilities of neural networks,” IEEE Transactions on Systems, Man, and Cybernetics, 19 (2), 299–314, 1989.
Stockburger, D. W., Multivariate Statistics: Concepts, Models, and Applications, http://www.psychstat.smsu.edu/, 1997.
Taguchi, G., Introduction to Quality Engineering, Asian Productivity Organization: Tokyo, 1986.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Oh, S., Han, J., Cho, H. (2001). Intelligent Process Control System for Quality Improvement by Data Mining in the Process Industry. In: Braha, D. (eds) Data Mining for Design and Manufacturing. Massive Computing, vol 3. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-4911-3_12
Download citation
DOI: https://doi.org/10.1007/978-1-4757-4911-3_12
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-5205-9
Online ISBN: 978-1-4757-4911-3
eBook Packages: Springer Book Archive