Neural network mode ling of a plate hot-rolling process

Ertan Oznergiz*, Kayhan Giilez, Can Ozsoy, Ayhan Kural

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

The force, torque and slab temperature models of each pass in the plate hotrolling process are established in this paper. In the first, an experimental model of a plate hot-rolling is represented. The structure of this model is in the neural network forms and predicts the steady-state values of force, torque and slab temperature. In the second part, the proposed dynamical models are compared with the classical empiric models commonly used in the rolling practice. The experimental data obtained from Eregli Iron and Steel Factory in Turkey was used for developing both of the models.

Original languageEnglish
Pages (from-to)17-22
Number of pages6
JournalIFAC-PapersOnLine
Volume37
Issue number15
DOIs
Publication statusPublished - 2004
Event11th IFAC Symposium on Automation in Mining, Mineral and Metal Processing, MMM 2004 - Nancy, France
Duration: 8 Sept 200410 Sept 2004

Bibliographical note

Publisher Copyright:
© 2004 lFAC.

Keywords

  • Experimental Modeling
  • Hot-rolling process
  • Identification
  • Neural networks

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