Researchers have developed an automated and reproducible workflow using the Galaxy scientific workflow environment to identify cracks and assess damage in fusion materials from scanning electron microscopy (SEM) images. This system processes SEM images and experimental metadata to quantify damage and ensure reproducibility by retaining intermediate products and processing history. The workflow is designed to operate without image-specific parameter tuning across various tungsten grades, microstructures, magnifications, and damage states, providing standardized damage descriptors for downstream machine-learning prediction and physics-based simulations. AI
IMPACT This workflow could accelerate fusion materials research by automating image analysis and providing data for predictive models.
RANK_REASON The item is an academic paper detailing a new methodology for materials science research. [lever_c_demoted from research: ic=1 ai=1.0]
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