Neural networks analysis of steel plate processing augmented by multi-objective genetic algorithms

F. Pettersson*, N. Chakraborti, S. B. Singh

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

22 Citations (Scopus)

Abstract

An earlier neural network analysis of processing of steel plates through hot rolling was subjected to a further refined analysis through some flexible neural networks that evolved using a multi-objective predator-prey genetic algorithm. The original data set expressing the Yield Strength and Ultimate Tensile Strength of the rolled slabs in terms of a total of 108 process variables were subjected to a systematic pruning through this evolutionary approach, till the nitrogen content of the steel emerged as the most significant input variable. A theoretical explanation is provided for this slightly unexpected result.

Original languageEnglish
Pages (from-to)890-898
Number of pages9
JournalSteel Research International
Volume78
Issue number12
DOIs
Publication statusPublished - 2007
MoE publication typeA1 Journal article-refereed

Keywords

  • Genetic algorithms
  • Multi-objective optimization
  • Neutral net

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