Development of neural network-based models to predict mechanical properties of hot dip galvanised steel coils

  1. González-Marcos, A. 1
  2. Alba-Elias, F. 1
  3. Castejón-Limas, M. 2
  4. Ordieres-Meré, J. 3
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

  2. 2 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  3. 3 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

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Revista:
International Journal of Data Mining, Modelling and Management

ISSN: 1759-1163

Año de publicación: 2011

Volumen: 3

Número: 4

Páginas: 389-405

Tipo: Artículo

DOI: 10.1504/IJDMMM.2011.042936 SCOPUS: 2-s2.0-84858393212 GOOGLE SCHOLAR

Otras publicaciones en: International Journal of Data Mining, Modelling and Management

Resumen

In the industrial arena, artificial neural networks are among the most significant techniques in system modelling because of their efficiency and simplicity. In this paper, we present an application of artificial neural networks, along with other techniques stemming from data mining, to model the yield strength, tensile strength, elongation, strain hardening coefficient and the Lankford's anisotropy coefficient of galvanised steel coils, according to the manufacturing process data. In particular, we propose the use of these models to improve the current control systems of hot-dip galvanising lines since an open loop control strategy must be adopted because the mechanical properties of hot-dip galvanising coils are not directly measurable. Copyright © 2011 Inderscience Enterprises Ltd.