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Meeting

2026 TMS Annual Meeting & Exhibition

Symposium

AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling

Presentation Title

Accelerated Prediction of Metallic deformation by Crystal Plasticity informed Machine Learning Models: Application to Dual Phase Steels

Author(s)

Satyapriya Gupta, Santoshkumar, Suresh Otageri, Yash Kavishwar

On-Site Speaker (Planned)

Satyapriya Gupta

Abstract Scope

We made an effort to accelerate the crystal plasticity (CP) model predictions of macro and grain level deformation behavior of a dual phase (DP) steel using Machine Learning (ML) models. We use experimentally validated CP model predictions to generate ML training datasets. To inform microstructure-property relation to ML dataset for DP steels, synthetic RVEs were generated using Dream3d with controlled microstructural variations. These RVEs were used to perform CP simulations using DAMASK under multiple loading conditions. Postprocessing was performed to prepare the training dataset used for training of ML models. The trained ML models showed excellent predictive capability for both macroscopic as well as grain-level deformation for a completely randomized RVE. This ML accelerated CP modeling approach enables accurate, data driven stress prediction and yield behaviour analysis, offering a robust framework for the accelerated design to high performance metallic alloys.

Proceedings Inclusion?

Planned:

Keywords

Modeling and Simulation, Machine Learning, Mechanical Properties

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