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Tool Wear Prediction in Milling Using Neural Networks

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Artificial Neural Networks — ICANN 2002 (ICANN 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2415))

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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.

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References

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Author information

Authors and Affiliations

  1. Instituto de Automática Industrial, CSIC, km. 22,800 N-III, La Poveda, 28500, Madrid, Spain

    Rodolfo E. Haber, A. Alique & J. R. Alique

  2. School of Computer Science and Engineering, Universidad Autónoma de Madrid, Ciudad Universitaria de Cantoblanco, Ctra. de Colmenar Viejo, km 15, 28049, Madrid, Spain

    Rodolfo E. Haber

Authors
  1. Rodolfo E. Haber
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  2. A. Alique
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  3. J. R. Alique
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Editor information

Editors and Affiliations

  1. ETS Informática, Universidad Autónoma de Madrid, 28049, Madrid, Spain

    José R. Dorronsoro

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© 2002 Springer-Verlag Berlin Heidelberg

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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

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  • DOI: https://doi.org/10.1007/3-540-46084-5_131

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44074-1

  • Online ISBN: 978-3-540-46084-8

  • eBook Packages: Springer Book Archive

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