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U-PolyConformer: Spatiotemporal machine learning for microstructure engineering
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DOI：10.1016/j.ijplas.2025.104597.png)
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Abstract
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• A novel spatiotemporal CNN-Transformer machine learning framework is proposed. • The first framework that captures full-field stress and strain evolution under arbitrary loading conditions. • Exhibits strong generalization; accurately capturing loading conditions, microstructures, and hardening outside the training distributions. • A novel microstructure optimization framework is employed to enhance formability by delaying localization.
Keywords:
Crystal plasticity finite element method
machine learning
Localized Deformation
Formability
Microstructure Engineering
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