Abstract
Neural networks are potential tools that can be used to improve process quality control. In fact, various neural algorithms have been applied successfully for detecting groups of well-defined unnatural patterns in the output measurements of manufacturing processes. This paper discusses the use of a neural network as a means for recognising changes in the state of the monitored process, rather than for identifying a restricted set of unnatural patterns on the output data. In particular, a control algorithm, which is based on the Fuzzy ART neural network, is first presented, and then studied in a specific reference case by means of Monte Carlo simulation. Comparisons between the performances of the proposed neural approach, and those of the CUSUM control chart, are also presented in the paper. The results indicate that the proposed neural network is a practical alternative to the existing control schemes.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Montgomery, D. C. (2000) Introduction to Statistical Quality Control, 4th ed J. Wiley, New York.
Zorriassantine, F., Tannock, J. D. T. (1998) A review of neural networks for statistical process control. J. of Intelligent Manufacturing 9:209–224.
Hwarng, H. B. and Hubele, N. F. (1993) Back-propagation pattern recognizers for X-bar control charts: methodology and performance. Comp. & Ind Eng., 24:219–235.
Smith, A. E. (1994) X-bar and R control chart interpretation using neural cornputing. Int J. of Production Research, 32: 309–320.
Guh, R S., Tannock, J. D. T. (1999) Recognition of control chart concurrent patterns using a neural network approach. Int J. of Production Research, 37(8): 1743–1765.
Cook, D. F., Zobel, C. W., Nottingham, Q. J. (2001) Utilization of neural networks for the recognition of variance shifts in correlated manufacturing process parameters, Int J. of Production Research, 39(17): 3881–3887.
Hwarng, H. B., Chong, C. W. (1995) Detecting process non-randomness through a fast and cumulative learning ART-based pattern recognizer. Int J. of Production Research, 33, 1817–1833.
Al-Ghanim, A. (1997) An unsupervised learning neural algorithm for identifying process behavior on control charts and a comparison with supervised learning approaches. Comp. & Ind Eng., 32(3): 627–639.
Pacella, M., Semeraro, Q, Anglani, A, (2002) On the use of adaptive resonance theory based neural algorithms for manufacturing process quality control. Submitted to Int J. of Production Research.
Huang, J., Georgiopoulos M., Heileman, J. L. (1995): Fuzzy ART Proprieties. Neural Networks, 8(2): 203–213.
Georgiopoulos, M., Femlund, H., Bebis, G., Heileman, G. L. (1996) Order of search in Fuzzy ART and Fuzzy ARTMAP: effect of the choice parameter. Neural Networks, 9(9), 1541–1559.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Wien
About this paper
Cite this paper
Pacella, M., Semeraro, Q., Anglani, A. (2003). Manufacturing process quality control by means of a Fuzzy ART neural network algorithm. In: Pearson, D.W., Steele, N.C., Albrecht, R.F. (eds) Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-0646-4_15
Download citation
DOI: https://doi.org/10.1007/978-3-7091-0646-4_15
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-00743-3
Online ISBN: 978-3-7091-0646-4
eBook Packages: Springer Book Archive