Modelling a skin-pass rolling process by means of data mining techniques and finite element method

  1. Escribano, R. 1
  2. Lostado, R. 1
  3. Martínez-de-Pisón, F.J. 1
  4. Pernía, A. 1
  5. Vergara, E. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Revue:
Journal of Iron and Steel Research International

ISSN: 1006-706X

Année de publication: 2012

Volumen: 19

Número: 5

Pages: 43-49

Type: Article

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DOI: 10.1016/S1006-706X(12)60098-3 SCOPUS: 2-s2.0-84862900199 WoS: WOS:000307202000007 GOOGLE SCHOLAR

D'autres publications dans: Journal of Iron and Steel Research International

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Aplicación de técnicas de Data Mining a la calificación inteligente de parámetros de calidad en bobinas de acero a la entrada de una linea de galvanizado en caliente

2007/00079/001

Résumé

An experience is presented using the finite element method (FEM) and data mining (DM) techniques to develop models that can be used to optimize the skin-pass rolling process based on its operating conditions. A FE model based on a real skin-pass process is built and validated. Based on this model, a group of FE models is simulated with different adjustment parameters and with different materials for the sheet; both variables are chosen from pre-set ranges. From all FE model simulations, a database is generated; this database is made up of the above mentioned adjustment parameters, sheet properties and the variables of the process arising from the simulation of the model, Various types of data mining algorithms are used to develop predictive models for each of the variables of the process. The best predictive models can be used to predict experimentally hard-to-measure variables (internal stresses, internal strains, etc.) which are useful in the optimal design of the process or to be applied in real time control systems of a skin-pass process in-plant. © 2012 Central Iron and Steel Research Institute.