cs.LGOct 2, 2026

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

Authors: Rinkle Juneja, Viktor Reshniak, Richard K. Archibald, John W. Duggan, Gregory R. Watson, Cory D. Hauck, Gary M. Staebler

Organizations: Oak Ridge National Laboratory, Oak Ridge, TN 37831

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Abstract

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.

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Figure 1: Overview of the collected surface-scan dataset. (a) Number of experiments and images per material, with image counts split into longitudinal (L), recrystallized (R), and transverse (T) cuts. (b) Pixel resolution distribution across image scales. (c) Example scans at fine and coarse scales. (d) Dataset coverage across materials and exposure conditions.
Figure 2: Galaxy display of results from the automated crack identification tool. The interface shows the original microscopy image, the ridge enhanced image used for segmentation, the intensity histogram with the automatically selected threshold, the detected cracks overlaid on the original image, the binary crack mask, and the one pixel wide skeleton of the detected crack network. The Galaxy history shown at the side records the input files, processing steps, parameter settings, and generated outputs.
Figure 3: Image processing steps and corresponding intensity histograms. In each histogram, the horizontal axis represents normalized pixel intensity and the vertical axis represents the number of pixels. The annotations identify representative image specific intensity artifacts and the automatically selected threshold used to generate the crack masks shown in Figure 4 .
Figure 4: Top: representative experimental SEM characterization for a single heat-flux and base-temperature condition across alloys and microstructures. Bottom: corresponding automated crack extraction used by the workflow.
Figure 5: Top row: Representative SEM images acquired at different physical pixel resolutions, with overlaid patch boundaries corresponding to a fixed physical size of 20μm20\mu\mathrm{m} . Physical pixel resolution is defined as the physical length represented by one pixel. Bottom row: Representative patches extracted from these images. Patches with coarse physical pixel resolution contain too few pixels across the fixed physical region to resolve crack and surface morphology reliably and are therefore excluded from morphology analysis.
Figure 6: Embedding pipeline for DINOv2 patch features. From left to right: PCA explained-variance decay used to assess linear compression; projection onto the first two principal components; distribution of local intrinsic-dimension estimates; and the first two diffusion-map coordinates illustrating nonlinear neighborhood structure.
Figure 7: Example from the Galaxy workflow builder showing the end goal - a fully reproducible experiment steering workflow.
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