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This paper introduces an automated and reproducible workflow for crack identification and damage assessment in fusion materials using scanning electron microscopy images. The workflow, implemented in the Galaxy scientific workflow environment, addresses the scalability limitations of manual analysis by processing SEM images and metadata to generate crack masks, skeletonized networks, and scalar damage descriptors. The method demonstrates robustness across various tungsten grades, microstructures, magnifications, and damage states, providing standardized inputs for downstream machine learning and physics-based simulations.
Automated crack identification in fusion materials now scales across diverse conditions without manual tuning, enabling standardized inputs for predictive modeling.
Post-exposure microscopy is central to qualification of fusion materials. However, manual analysis does not scale to the volume, heterogeneity, and multiresolution character of modern fusion-materials campaigns. To address this challenge, we present a reproducible workflow, implemented in the Galaxy scientific workflow environment, for automated crack identification and quantitative damage assessment from scanning electron microscopy images. The workflow processes SEM images and experimental metadata to identify cracks, quantify damage, and retain the intermediate products and processing history needed for reproducibility. Outputs include crack masks, skeletonized crack networks, quality-control visualizations, and scalar damage descriptors. The method is designed to operate without image-specific parameter tuning across tungsten grades, microstructures, magnifications, and damage states. We demonstrate the workflow on a sparse electron-beam thermal-shock dataset containing 418 images from 114 experiments spanning five tungsten grades and three microstructural states. We define a crack-density descriptor, which provides standardized inputs for downstream machine-learning prediction and physics-based crack simulation. These predictive components are exposed in the same Galaxy environment and are intentionally treated here as extensible workflow modules. The principal contribution is therefore an end-to-end, shareable, and computationally portable workflow that links experimental characterization, automated image analysis, preliminary damage prediction, and simulation-guided data acquisition for fusion-materials research.