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

2026-10-02

AI Summary

This study addresses the bottleneck of manually processing massive, heterogeneous, and multi-resolution images in the microstructural analysis of fusion materials. We construct an end-to-end automated workflow on the Galaxy platform to extract crack features from scanning electron microscopy (SEM) images and quantify radiation-induced damage. The core innovations include a tuning-free, cross-material adaptive method and a standardized crack density descriptor that seamlessly bridges machine learning predictions with physics-based simulations. Validated on a dataset comprising 418 images across five tungsten grades, the proposed approach consistently generates crack masks, skeleton networks, and high-quality visualizations. Ultimately, this work enables fully reproducible, automated analysis throughout the entire characterization pipeline.

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.

Problem

• fusion materials
• crack identification
• damage assessment
• scanning electron microscopy
• reproducibility

Innovation

• Automated crack identification
• Reproducible workflow
• Galaxy environment
• Damage assessment
• Crack-density descriptor