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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

A transfer learning approach for predicting fatigue behavior in additively manufactured metals

Author(s)

Rebecca Divine, Nathan Searle, Krishna Logakannan, Ashley Lenau, Ashley Spear, Remi Dingreville

On-Site Speaker (Planned)

Rebecca Divine

Abstract Scope

Additively manufactured (AM) metals exhibit spatial variability in microstructural features, causing their effective mechanical properties to differ throughout a part. For fatigue applications, predicting location-dependent fatigue properties in AM components could enable identification of life-limiting regions and inform design and qualification strategies. Here, we leveraged a pre-trained deep learning (DL) model on a transfer-learning task of mapping microstructural images in AM SS316L to location-dependent fatigue properties. The DL model was first trained to predict homogenized stress-strain response given 3D images of microstructural subvolumes within an AM domain. Transfer learning was then applied to predict the evolution of fatigue indicator parameters under fully reversed loading. This work demonstrates the transferability of a DL model trained from relatively simple (monotonic) behavior to more complex behavior, enabling rapid predictions of location-dependent fatigue performance in AM components. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

Proceedings Inclusion?

Planned:

Keywords

Additive Manufacturing, Machine Learning,

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