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Towards the Modeling of the Hot Rolling Industrial Process. Preliminary Results

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Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12489))

Abstract

In the paper we describe the industrial process of hot rolling of steel. In cooperation with ArcelorMittal Poland we consider a specific fully automated production line. While it is equipped with a number of industrial sensors, the acquired data has only been analyzed on a basic statistical level, mainly for reporting. In the paper we outline opportunities for the use of AI methods in order to improve the process and possibly the quality of the resulting product. We report on preliminary results using selected methods of eXplainable AI.

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Notes

  1. 1.

    See: https://scikit-learn.org.

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Acknowledgments

This paper is funded by the National Science Centre, Poland under CHIST-ERA programme, the CHIST-ERA 2017 BDSI PACMEL Project, NCN 2018/27/Z/ST6/03392. We would like to thank ArcelorMittal Poland for cooperation. Special thanks go to the department of Automation, Industrial Informatics and Models in the Krakow steel plant.

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Authors and Affiliations

  1. Jagiellonian University, AGH University of Science and Technology, Krakow, Poland

    Maciej Szelążek, Szymon Bobek & Grzegorz J. Nalepa

  2. Computer Science Department, Universidad Rey Juan Carlos, Madrid, Spain

    Maciej Szelążek, Szymon Bobek & Antonio Gonzalez-Pardo

Authors
  1. Maciej Szelążek
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  2. Szymon Bobek
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  3. Antonio Gonzalez-Pardo
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  4. Grzegorz J. Nalepa
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Corresponding author

Correspondence to Szymon Bobek .

Editor information

Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Szelążek, M., Bobek, S., Gonzalez-Pardo, A., Nalepa, G.J. (2020). Towards the Modeling of the Hot Rolling Industrial Process. Preliminary Results. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_34

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  • DOI: https://doi.org/10.1007/978-3-030-62362-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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