Skip to main content

Classifying the wear of turning tools with neural networks

  • Part VII: Prediction, Forecasting, and Monitoring
  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1327))

Abstract

The increasing extent of automation in manufacturing processes requires flexible and reliable tool monitoring systems. One of the most important and most difficult tasks in this context is the on-line supervision of a tool's wear. Considering the state of wear and the actual working process (e.g. rough or finish turning) it is possible to exchange a tool just in time, which offers significant economic advantages. This paper presents a new method to classify a characteristic wear parameter by means of neural networks. In order to find an appropriate network paradigm, multilayer perceptrons, Fuzzy ARTMAPS, self-organizing maps and NEFCLASS networks are investigated. The input parameters of the networks are process-specific parameters (like the feed rate or the cutting speed) and specific coefficients extracted from three measured force signals.

This is a preview of subscription content, log in via an institution.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bukkapatnam, S. T. S.; Kumara, S. R. T.; Lakhtakia, A.: Fractal Estimation of Flank Wear in Turning Using Time-Delay Neural Networks; in: Intelligent Engineering Systems through Artificial Neural Networks (eds. Dagli, C. H. et al.); vol. 4; ASME Press, New York, 1994; (Proceedings of the Artificial Neural Networks in Engineering (ANNIE' 94) Conference)

    Google Scholar 

  2. Das, S.; Chattopadhyay, A. B., Murthy, A. S. R.: Force Parameters for Online Tool Wear Estimation: A Neural Network Approach; in: Neural Networks; vol. 9 (9), 1996

    Google Scholar 

  3. Golz, H. U.; Schillo, E.; Wolf, A.; Kaufeld, M.; Sprengel, P.; Johannsen, P.; Heinek, D.: Bewertung von aus Sicht der Anwender; in: Überwachung von Zerspan-und Umformprozessen; VDI-Verlag, Düsseldorf, 1995; (Proceedings of the CIRP/VDI conference, VDI Berichte no. 1179)

    Google Scholar 

  4. Klauss, W.: Das richtige System — Erfahrungen beim Drehen mit Werkzeugüberwachungssystemen; in: fertigung, nov. / dec. 1995

    Google Scholar 

  5. Leem, C. S.; Dornfeld, D. A.; Dreyfus, S. E.: A Customized Neural Network for Sensor Fusion in On-Line Monitoring of Cutting Tool Wear; in: Journal of Engineering for Industry (Transactions of the ASME); vol. 117, may 1995

    Google Scholar 

  6. Li, S.; Elbestawi, M. A.: Tool Condition Monitoring in Machining by Fuzzy Neural Networks; in: Journal of Dynamic Systems, Measurement and Control (Transactions of the ASME); vol. 118, dec. 1996

    Google Scholar 

  7. Neural Ware, Inc.; Pittsburgh (PA); Neural Computing — A Technology Handbook for Neural-Works Professional II/PLUS and Neural-Works Explorer, 1993

    Google Scholar 

  8. Nauck, D.; Kruse, R.: NEFCLASS — A Neuro-Fuzzy Approach for the Classification of Data; in: Applied Computing (eds. George, K. M. et al.); ACM Press, 1995, (Proceedings of the 1995 ACM Symposium on Applied Computing)

    Google Scholar 

  9. Nauck, D.; Nauck, U.; Kruse, R.: Generating Classification Rules with the Neuro-Fuzzy System NEFCLASS; in: Proceedings of the Biennal Conference of the North American Fuzzy Information Processing Society (NAFIPS'96), 1996

    Google Scholar 

  10. Rojas, R.: Neural Networks — A Systematic Introduction; Springer-Verlag, Berlin, Heidelberg, New York, 1996

    Google Scholar 

  11. Shaw, M. C.: Metal Cutting Principles; Oxford University Press, Oxford, 1989

    Google Scholar 

  12. Warnecke, G.; Müller, M.: Identification and Monitoring of Cutting Processes by Artificial Neural Networks; in: Intelligent Engineering Systems through Artificial Neural Networks (eds. Dagli, C. H. et al.); vol. 4; ASME Press, New York, 1994; (Proceedings of the Artificial Neural Networks in Engineering (ANNIE' 94) Conference)

    Google Scholar 

  13. Zell, A.: Simulation Neuronaler Netze; Addison-Wesley, Bonn, Paris, Reading (MA), 1994

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Wulfram Gerstner Alain Germond Martin Hasler Jean-Daniel Nicoud

Rights and permissions

Reprints and permissions

Copyright information

© 1997 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Sick, B. (1997). Classifying the wear of turning tools with neural networks. In: Gerstner, W., Germond, A., Hasler, M., Nicoud, JD. (eds) Artificial Neural Networks — ICANN'97. ICANN 1997. Lecture Notes in Computer Science, vol 1327. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020293

Download citation

  • DOI: https://doi.org/10.1007/BFb0020293

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63631-1

  • Online ISBN: 978-3-540-69620-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics