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 language | English (US) |
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Article number | 494 |
Pages (from-to) | 1-13 |
Number of pages | 13 |
Journal | Metals |
Volume | 11 |
Issue number | 3 |
DOIs | |
State | Published - Mar 17 2021 |
Keywords
- Casting
- Multiphysics
- Neural networks
- Sequence deep learning
- Solidification
- Steel
ASJC Scopus subject areas
- General Materials Science