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

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

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

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.

Figures & tables

Panel of Figure 1 — 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.

Panel of Figure 3 — 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 .

Panel of 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.

Panel of Figure 5 — Figure 5: Top row: Representative SEM images acquired at different physical pixel resolutions, with overlaid patch boundaries corresponding to a fixed physical size of $20\mu\mathrm{m}$ . 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.

Microscopy Image Analysis

Microscopy image analysis uses computational methods to detect, segment, track, reconstruct, and quantify structures in scientific images.

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Compass: Degradation-Simulated Reciprocal Learning with Lightweight Needle RWKV for Multimodal Crack Segmentation under Missing Modalities

Hui Liu, Chen Jia, Fan Shi, Xu Cheng+2

Engineering Research Center of Learning-Based Intelligent System (Ministry of Education) Tianjin University of Technology Tianjin, China

Missing-Modality Learning Efficient Multimodal Inference Multimodal Fusion Crack Segmentation

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In multimodal crack segmentation for industrial facilities, the key challenge is preventing missing modalities from degrading pixel-level performance while maintaining low computational cost. Existing methods struggle to address semantic degradation caused by missing modalities. We propose Compass, a lightweight network for robust crack segmentation under arbitrary missing modalities. Compass comprises Degradation Simulation Distillation (DSD), Needle Block, and Evidential Topology-Preserving Fusion (ETPF). DSD constructs a degradation simulation stream that mimics more severe missing conditions and performs reciprocal distillation with the original stream, decoupling complete perception from degradation adaptation. Within DSD, Feature-Aware Prototype Transmitter (FAPT) performs modality agnostic prototype-guided feature completion to maintain semantic integrity under incomplete modality conditions. As a lightweight backbone, Needle injects crack-direction cues into WKV modulation and combines connectivity-aware gating with anisotropic context probing for structure-aware modeling. ETPF fuses multimodal features via Dempster-Shafer evidential combination with uncertainty-gated decoding, preserving crack topology while suppressing unreliable features. Experiments on three datasets demonstrate state-of-the-art (SOTA) performance under diverse missing modality scenarios. Even with 90% depth modality missing on CrackDepth, Compass achieves F1 of 0.8216 and mIoU of 0.8434 with only 2.58M parameters. The code is available at https://github.com/Karl1109/Compass.

Abstract

Microscopy Image Analysis

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