arXiv：cs.LG· Rinkle Juneja, Viktor Reshniak, Richard K. Archibald, John W. Duggan, Gregory R. Watson, Cory D. Hauck, Gary M. Staebler·· 3 小时前AI 评分30
基于 Galaxy 的聚变材料裂纹识别与损伤评估自动化工作流
An Automated and Reproducible Workflow for Crack Identification and Damage Assessment of Fusion Materials
AI 导读
研究者提出一套在 Galaxy 科学工作流环境中实现的可复现流程，从扫描电子显微镜图像中自动识别裂纹并定量评估损伤，输出裂纹掩码、骨架化裂纹网络、质控可视化及标量损伤描述符。该方法无需针对图像调参，已在包含 418 张图像、114 次实验、覆盖五种钨牌号与三种微观结构状态的电子束热冲击数据集上验证，并提出裂纹密度描述符供下游机器学习预测与物理裂纹模拟使用。
正文
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.
| Subjects: | Machine Learning (cs.LG); Materials Science (cond-mat.mtrl-sci) |
| Cite as: | arXiv:2610.03505 [cs.LG] |
| (or arXiv:2610.03505v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.03505 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Rinkle Juneja [view email]
[v1]
Fri, 2 Oct 2026 16:02:36 UTC (11,980 KB)
来源：arXiv：cs.LG · arxiv.org