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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Neural Modeling of Anisotropic Yield Behavior Under Plasticity Constraints
Author(s) Anderson Nascimento, Irene Beyerlein
On-Site Speaker (Planned) Anderson Nascimento
Abstract Scope Conventional anisotropic yield criteria can effectively describe plastic flow in metals but often require challenging parameter identification and can suffer from non-unique coefficient sets. Machine-learning surrogates however can provide greater flexibility but typically lack the mathematical structure required of a valid yield function. We introduce a neural architecture in which convexity, positive homogeneity, and smoothness are enforced by construction, ensuring admissibility for any stress state. The network is trained on a compact set of virtual stress states derived from established phenomenological models and is designed to reproduce both yield stresses and associated flow directions. Smoothness is assessed through continuous uniaxial r-values, while applications to different crystal structures serve as test cases for representing a wide range of anisotropic behavior. An uncertainty-based sampling strategy is outlined to reduce dataset requirements and enable adaptive refinement of the learned yield surface.
Proceedings Inclusion? Undecided

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