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. © Springer-Verlag Berlin Heidelberg 2002.
Cite
CITATION STYLE
Haber, R. E., Alique, A., & Alique, J. R. (2002). Tool wear prediction in milling using neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 807–812). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_131
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