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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title μSAM-Based Inference of Nuclear Materials Processing History from SEM Imagery
Author(s) Mayolo Valencia Mendoza, Alexei Skurikhin, Judith Cohn, Luther McDonald, Kari Sentz
On-Site Speaker (Planned) Kari Sentz
Abstract Scope Particle and microstructure morphology of nuclear materials from scanning electron microscopy (SEM) is an emerging signature with the potential for the identification of key factors of processing history. We introduce a method for inferring the calcination temperature of nuclear materials without requiring image annotations. Leveraging foundation segmentation models, Segment Anything Model (SAM) and its microscopy-tuned variant, μSAM, we aggregate shape features from all segmented particles in each image. In contrast to supervised approaches that train models to detect only a small set of particles, our method exploits the statistical benefits of large sample sizes derived from all segmented particles in the image and enables the extraction of statistical summaries of particle shape characteristics. These summaries are sufficiently distinctive that logistic regression can categorize calcination temperature classes with high accuracy. Our approach achieved 92.1% ± 4.3% accuracy in predicting calcination temperatures categories in test data, outperforming the previous method by 8.8%.
Proceedings Inclusion? Undecided

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