Skip to main content

A hardware implementation of hierarchical Neural Networks for real-time quality control systems in industrial applications

  • Part VIII: Implementations
  • Conference paper
  • First Online:
Artificial Neural Networks — ICANN'97 (ICANN 1997)

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

Included in the following conference series:

Abstract

In this paper a real-time quality control system for steel industry is presented. The system implements the surface defect classification of steel strips in flat rolled mills in real-time. To achieve reliable classification accuracy the system implements a MLP_based hierarchical neural network. A dedicated hardware implementation has been designed and manufactured to meet the realtime constraints of the application. An ASIC neural chip directly implements the neural network and it is integrated on a custom high speed co-processor board, compatible with many commercial carrier board. The entire system has been tested with data coming from the plant.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R. P. Lippmann:An introduction to computing with neural nets. IEEE ASSP 4 (1987) 4–22.

    Google Scholar 

  2. Daniela Baratta, Maurizio Valle and Daniele D. Caviglia: Hierarchical Neural Networks for Quality Control in Steel-Industry Plants. Journal of Microelectronic Systems Integration.

    Google Scholar 

  3. Daniela Baratta, Gian Marco Bo, Maurizio Valle and Daniele D. Caviglia: A Neural ASIC for Real-Time Quality Control in Industrial Applications. Proc. of EMAC'97 (1997) 14–17.

    Google Scholar 

  4. R. Parenti, A. M. Colla: Neural Networks Applications in Process Control. Proc. of SNN'97 (1997) Invited Paper.

    Google Scholar 

  5. D. Caviglia, M. Valle, D. Baratta, V. Baiardo, M. Marchesi: A Neural ASIC Architecture for Real-Time Classification. Proc. of ELJROMICRO95 (1995) 632–638.

    Google Scholar 

  6. P. Vogl, J. K. Mangis, W. T. Zink, D. L. Alkon, “Accelerating the convergence of the Back Propagation method,” Biological Cybernetics, 59 (1988) 257–263.

    Google Scholar 

  7. E. Rumelhart, G. E. Hinton, R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing, MIT Press 1 1986 362–381.

    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

Baratta, D. et al. (1997). A hardware implementation of hierarchical Neural Networks for real-time quality control systems in industrial applications. 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/BFb0020319

Download citation

  • DOI: https://doi.org/10.1007/BFb0020319

  • 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