Tool Wear Prediction in Milling Using Neural Networks

Authors:
Rodolfo E. Haber;A. Alique;José R. Alique
Affiliations:
-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 3
Cited 0

Practical methods of optimization; (2nd ed.)

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Fuzzy supervisory control of end milling process

Information Sciences: an International Journal
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation

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Abstract

An intelligent supervisory system, which is supported on a modelbased 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.