Chapter 20: Data Mining Experiences in Steel Industry

  1. Ordieres-Meré, J. 1
  2. González-Marcos, A. 2
  3. Castejón-Limas, M. 3
  4. Martínez-de-Pisón, F.J. 2
  1. 1 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

  2. 2 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  3. 3 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

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Libro:
Handbook Of Research On Machine Learning Applications and Trends: Algorithms, Methods and Techniques

Editorial: information science reference

ISBN: 978-1-60566-766-9

Año de publicación: 2010

Páginas: 427-439

Tipo: Capítulo de Libro

SCOPUS: 2-s2.0-84898344043 GOOGLE SCHOLAR

Resumen

This chapter reports five experiences in successfully applying different data mining techniques in a hotdip galvanizing line. Engineers working in steelmaking have traditionally built mathematical models either for their processes or products using classical techniques. Their need to continuously cut costs down while increasing productivity and product quality is now pushing the industry into using data mining techniques so as to gain deeper insights into their manufacturing processes. The authors' work was aimed at extracting hidden knowledge from massive data bases in order to improve the existing control systems. The results obtained, though small at first glance, lead to huge savings at such high volume production environment. The effective solutions provided by the use of data mining techniques along these projects encourages the authors to continue applying this data driven approach to frequent hard-to-solve problems in the steel industry. © 2010, IGI Global.