AI 中文总结
针对传统钢材疲劳寿命测试耗时长的问题，提出结合物理信息特征与CNN的CV框架，通过显微图像预测疲劳寿命及不确定性，在合成基准上取得高精度且校准效果提升，已开源相关工具。
AI 中文摘要
传统评估结构钢疲劳寿命需耗时数十至数百小时的力学测试，无法满足快速质量控制的需求。本文提出CV（计算机视觉）框架，可直接通过光学显微图像估算轻质合金钢的疲劳寿命（$\boldsymbol{\rm \bf \textit{log} N_f}$），无需物理测试。\n该流程包含七步OpenCV预处理步骤以去除伪影、28维物理信息特征提取器（量化裂纹形貌、晶粒结构、孔隙率和织构），以及采用高斯负对数似然（GNLL）损失训练的CNN回归模型，可同时预测$\boldsymbol{\rm \bf \textit{log} N_f}$和样本特定不确定性$\boldsymbol{\rm \textbf{σ̂}}$。\n在合成显微图像基准上测试三种架构（SE-CNN、ResNet-50、VGG-16），ResNet-50取得$R^2=0.93$、RMSE=0.18对数循环次数、macro-F1=0.91的结果。GNLL目标函数相比均方误差基线将预期校准误差降低76%（ECE：$0.089 \rightarrow 0.021$）。Grad-CAM图证实网络关注的是冶金学上有意义的微观结构特征。\n该流程单张图像运行时间不足65ms，流程代码与合成数据集生成器已开源。由于验证完全基于合成显微图像，结果证明了该方法在模拟条件下的合理性；针对实际现场样本的域迁移研究是下一步的工作重点。
英文摘要
Here is the plain text version optimized for arXiv's submission form. Custom macros (like \CV and \SI) have been converted to standard text/math so they render correctly on the webpage: Evaluating the fatigue life of structural steels conventionally requires mechanical testing lasting tens to hundreds of hours, making it impractical for rapid quality control. We present CV, a computer vision framework that estimates the fatigue life ($\log N_f$) of lightweight alloy steels directly from optical micrographs without physical testing.The pipeline features a seven-stage OpenCV preprocessing routine to remove artifacts, a 28-dimensional physics-informed feature extractor (quantifying crack morphology, grain structure, porosity, and texture), and a CNN regression model trained with a Gaussian negative log-likelihood (GNLL) loss to jointly predict $\log N_f$ and sample-specific uncertainty $\hatσ$.Evaluating three architectures (SE-CNN, ResNet-50, VGG-16) on a synthetic micrograph benchmark, ResNet-50 achieves $R^2 = 0.93$, RMSE = 0.18 log-cycles, and macro-F1 = 0.91. The GNLL objective reduces Expected Calibration Error by 76% compared to a mean-squared-error baseline (ECE: $0.089 \rightarrow 0.021$). Grad-CAM maps confirm the network attends to metallurgically meaningful microstructural features.Running in under 65 ms per image, the pipeline and synthetic dataset generator are open-sourced. Because validation relies entirely on synthetic micrographs, these results demonstrate methodological soundness under simulated conditions; a domain-transfer study on real field samples is the immediate next step.
