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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

Multimodal Spatial Encoding of Metal Microstructure and Plasticity for Mechanical Properties Prediction

Author(s)

M. Calvat, G. Sparks, K. Vecchio, J.C. Stinville

On-Site Speaker (Planned)

J.C. Stinville

Abstract Scope

To accelerate alloy design, rapid prediction of mechanical properties is essential, especially for time-intensive ones such as fatigue and creep. In this work, we propose a novel framework that encodes metal microstructure and plasticity under elementary loading conditions to train a machine learning model to predict macroscopic properties. A large, spatially resolved, and multi-modal dataset was generated, combining EBSD-based microstructural maps with high-resolution digital image correlation, measurements of plastic response under monotonic loading, short-term creep, and a few fatigue cycles. To effectively represent this complex data, we employed machine learning-based scanning probe encoding using variational autoencoders. They enable high-fidelity and low-dimensional representations of metal microstructures and plasticity fields. In parallel, macroscopic mechanical properties (fatigue, creep, and monotonic properties), were also measured for each alloy. We assessed prediction accuracy and demonstrated the benefits of incorporating both spatially encoded microstructural and plasticity information for the rapid prediction of mechanical properties.

Proceedings Inclusion?

Planned:

Keywords

Machine Learning, Mechanical Properties, Characterization

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