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Practical Application of Artificial Neural Networks in Designing Parameters of Steel Heat Treatment Processes

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7267))

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Abstract

The article is dedicated to the possibilities of practical application of artificial neural networks in designing parameters of steel vacuum carburization processes and preparing for cooling in high-pressure gas. In the following sections, the nature of vacuum carburization technology, the course of research on the precipitation phenomena, the construction of an artificial neural network and the algorithm of searching process parameters have been presented.

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Author information

Authors and Affiliations

  1. Institute of Materials Science and Engineering, Technical University of Lodz, Stefanowski St. 1/15, 90-924, Lodz, Poland

    Emilia Wołowiec & Piotr Kula

Authors
  1. Emilia Wołowiec
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  2. Piotr Kula
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Wołowiec, E., Kula, P. (2012). Practical Application of Artificial Neural Networks in Designing Parameters of Steel Heat Treatment Processes. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_23

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  • DOI: https://doi.org/10.1007/978-3-642-29347-4_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29346-7

  • Online ISBN: 978-3-642-29347-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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