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

Mostrar afiliacións +
Revista:
International Journal of Data Mining, Modelling and Management

ISSN: 1759-1163

Ano de publicación: 2011

Volume: 3

Número: 4

Páxinas: 389-405

Tipo: Artigo

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

Outras publicacións en: International Journal of Data Mining, Modelling and Management

Resumo

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.