Abstract
In this paper, the combined use of decision trees and artificial neural networks is examined in the area of quality improvement of industrial processes. The main goal is to achieve a better understanding of different settings of process parameters and to be able to predict more accurately the effect of different parameters on the final product quality. This paper also presents results from the application of the combined decision tree — neural network method to the transformer manufacturing industry. In the environment considered, quality improvement is achieved by increasing the classification success rate of transformer iron losses. The results from the application of the proposed method on a transformer industry demonstrate the feasibility and practicality of this approach for the quality improvement of industrial processes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sethi, I.K.: Entropy Nets: From Decision Trees to Neural Networks. Proceedings of the IEEE, 78(10), October 1990, pp. 1605–1613
Wehenkel, L., Pavella, M.: Decision trees and transient stability of electric power systems. Automatica, 27 (1), 1991, 115–134
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In Rumelhart, D.E., McClelland, J.L., (eds.): Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations, Cambridge, MA: M.I.T. Press, 1986
Wehenkel, L.: Automatic Learning Techniques in Power Systems. Kluwer Academic (1998)
Hatziargyriou, N.D., Georgilakis, P.S., Spiliopoulos, D.S., Bakopoulos, J.A.: Quality improvement of individual cores of distribution transformers using decision trees. Int. Journal of Engineering Intelligent Systems, 6 (3), September 1998, 29–34
AI Ware “NNET 210 User’s Manual”, AI Ware Incorporated, Cleveland, OH, 1989
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Georgilakis, P., Hatziargyriou, N. (2002). On the Application of Artificial Intelligence Techniques to the Quality Improvement of Industrial Processes. In: Vlahavas, I.P., Spyropoulos, C.D. (eds) Methods and Applications of Artificial Intelligence. SETN 2002. Lecture Notes in Computer Science(), vol 2308. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46014-4_42
Download citation
DOI: https://doi.org/10.1007/3-540-46014-4_42
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43472-6
Online ISBN: 978-3-540-46014-5
eBook Packages: Springer Book Archive