Improving Steel Industrial Processes Using Genetic Algorithms and Finite Element Method

  1. Sanz-García, A. 1
  2. Lostado-Lorza, R. 1
  3. Pernía-Espinoza, A. 1
  4. Martínez-De-Pisón-Ascacíbar, F.J. 1
  1. 1 Universidad de La Rioja
    info

    Universidad de La Rioja

    Logroño, España

    ROR https://ror.org/0553yr311

Libro:
Advances in Intelligent and Soft Computing, 87

Editorial: SPRINGER-VERLAG

ISBN: 978-3-642-19643-0

Año de publicación: 2011

Volumen: 87

Páginas: 233-241

Tipo: Capítulo de Libro

DOI: 10.1007/978-3-642-19644-7_25 SCOPUS: 2-s2.0-80052949908 WoS: WOS:000290975700025 GOOGLE SCHOLAR

Resumen

Steel industrial engineers must estimate optimal operational parameters of industrial processes and the correct model for complex material behaviour. Common practice has been to base these determinations on classic techniques, such as tables and theoretical calculations. In this paper three successful experiences combining finite element modelling with genetic algorithms are reported. On the one hand, two cases of improvement in steel industrial processes are explained; on the other hand, the efficient determination of realistic material behaviour laws is presented. The proposed methodology optimizes and fully automates these determinations. The reliability and effectiveness of combining genetic algorithms and the finite element method is demonstrated in all cases. © 2011 Springer-Verlag Berlin Heidelberg.