Abstract
An intelligent supervisory system, which is supported on a model-based approach, is presented herein. A model, created using Artificial Neural Networks (ANN), able to predict the process output is introduced in order to deal with the characteristics of such an ill-defined process. In order to predict tool wear, residuals errors are used as basis of a decision-making algorithm. Experimental tests are made in a professional machining center. The attained results show the suitability and potential of this supervisory system for industrial applications.
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
Haber R., Peres C., Alique J.R., Ros S. Fuzzy supervisory control of end milling process. Information Sciences 89 (1996) 95–106.
Haykin S., Neural Networks: A comprehensive foundation. 2nd edition, IEEE Press, 1999.
Fletcher R., Practical Methods of Optimization. 2nd. Edition, John Wiley&Sons (2000).
Isermann R. Process fault detection based modelling and estimation methods-A survey. Automatica 20(4) (1984) 387–404.
Tzafestas S., Watanabe K. Modern approaches to system sensor fault detection and Diagnosis. Journal A 31(4) (1990) 42–57.
Grabec I., Chaos generated by the cutting process, Physics Letter 117 (1986) 384–386.
Haber R. E., Haber R.H., Alique A., Ros S., Application of knowledge-based systems for supervision and control of machining processes. In: S.K. Chang (ed.): Handbook of Software Engineering and Knowledge Engineering Vol. II. World Scientific Publishing (2002) 327–362.
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
Haber, R.E., Alique, A., Alique, J.R. (2002). Tool Wear Prediction in Milling Using Neural Networks. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_131
Download citation
DOI: https://doi.org/10.1007/3-540-46084-5_131
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44074-1
Online ISBN: 978-3-540-46084-8
eBook Packages: Springer Book Archive