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arXiv Machine Learning By Rinkle Juneja, Viktor Reshniak, Richard K. Archibald, John W. Duggan, Gregory R. Watson, Cory D. Hauck, Gary M. Staebler

An Automated and Reproducible Workflow for Crack Identification and Damage Assessment of Fusion Materials

Read the original on arXiv Machine Learning →

The paper presents a reproducible workflow built in the Galaxy environment for automated crack identification and damage assessment from scanning electron microscopy images of fusion materials. It processes SEM images and metadata to generate crack masks, skeletonized networks, quality‑control visualizations, and scalar damage descriptors without needing image‑specific tuning across different tungsten grades, microstructures, magnifications, and damage states. The workflow was demonstrated on a dataset of 418 images from 114 experiments covering five tungsten grades and three microstructural states, and it includes a crack‑density descriptor that serves as a standardized input for downstream machine‑learning prediction and physics‑based crack simulation modules.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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