发表机构
Oak Ridge National Laboratory（橡树岭国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对聚变材料辐照后显微分析中人工处理无法扩展的问题，提出一种在Galaxy环境中实现的可复现自动化工作流，可从SEM图像识别裂纹并量化损伤，无需参数调整，并在稀疏数据集上验证，为后续预测与模拟提供标准化输入。
AI 中文摘要
辐照后显微分析是聚变材料资格认证的核心环节。然而，人工分析无法应对现代聚变材料试验中数据的体量、异质性和多分辨率特征。为应对这一挑战，我们提出了一种在Galaxy科学工作流环境中实现的可复现工作流，用于从扫描电子显微镜图像中自动识别裂纹并进行定量损伤评估。该工作流处理SEM图像和实验元数据，以识别裂纹、量化损伤，并保留实现可复现性所需的中间产物和处理历史。输出包括裂纹掩膜、骨架化裂纹网络、质量控制可视化以及标量损伤描述符。该方法设计为无需针对图像特定参数调整即可适用于不同钨牌号、微观结构、放大倍数和损伤状态。我们在一个稀疏电子束热冲击数据集上展示了该工作流，该数据集包含来自114个实验的418张图像，涵盖五种钨牌号和三种微观结构状态。我们定义了一个裂纹密度描述符，为下游机器学习预测和基于物理的裂纹模拟提供标准化输入。这些预测组件在同一Galaxy环境中公开，并在此有意作为可扩展工作流模块处理。因此，主要贡献是一个端到端、可共享且计算可移植的工作流，它将实验表征、自动化图像分析、初步损伤预测和模拟引导的数据采集联系起来，服务于聚变材料研究。
英文摘要
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.
