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Meeting TMS Specialty Congress 2026
Symposium 4th World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2026)
Presentation Title Generalist to Specialist Sequential Segmentation of Label-Free Low-Contrast Al-Si Solidification Video
Author(s) Nicholas Amano, Elizabeth A Holm
On-Site Speaker (Planned) Nicholas Amano
Abstract Scope In materials science, quantitative microstructural image segmentation is essential for analyzing phase behavior and solidification evolution, the primary drivers of material performance. General image foundation models like Meta’s Segment Anything Model (SAM 2) often struggle with the specialized details of microstructural data. This research addresses that gap by introducing a bootstrapping framework that leverages rough, unsupervised segmentations from SAM 2 to train a robust, Unet-based segmentation model. While SAM 2 may produce insufficient results for direct analysis, a small subset of its more accurate portions of frames can capture the representative features necessary to guide a supervised learner. By utilizing these initial outputs as training data, we trained a specialized model capable of high-fidelity analysis from minimal human-annotated input. We demonstrate this method’s efficacy by detecting Al-Si solidification fronts in extremely low-contrast optical images. The results show significant improvements in segmentation accuracy and enable meaningful microstructural characterization.
Proceedings Inclusion? Undecided

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