Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification

Seid Koric, Diab W. Abueidda

Research output: Contribution to journalArticlepeer-review

Abstract

The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challenging multiphysics approach is used on high-performance computing to generate sufficient training and testing data for subsequent deep learning. We have demonstrated how the innovative sequence deep learning methods can learn from multiphysics modeling data of a solidifying slice traveling in a continuous caster and correctly and instantly capture the complex history and temperature-dependent phenomenon in test data samples never seen by the deep learning networks.
Original languageEnglish (US)
Article number494
Pages (from-to)1-13
Number of pages13
JournalMetals
Volume11
Issue number3
DOIs
StatePublished - Mar 17 2021

Keywords

  • Casting
  • Multiphysics
  • Neural networks
  • Sequence deep learning
  • Solidification
  • Steel

ASJC Scopus subject areas

  • Materials Science(all)

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