用显微图像预测钢材疲劳寿命，无需物理实验。
Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning
- 结合物理知识的深度学习框架，从图像提取28维微观结构特征。
- 在合成数据上准确率达R²=0.93，误差仅0.18对数周期。
- 可快速输出寿命预测与置信度，适合工业质量检测场景。
传统钢材疲劳寿命评估需数十至数百小时的力学测试，难以用于快速质检。本文提出一种基于计算机视觉的框架CV，可直接从光学显微图像预测轻质合金钢的疲劳寿命（$\ ext{log } N_f$），无需物理测试。该流程包含七阶段OpenCV预处理以去除伪影，28维物理信息特征提取器（量化裂纹形貌、晶粒结构、孔隙率与纹理），以及采用高斯负对数似然（GNLL）损失训练的CNN回归模型，联合预测$\ ext{log } N_f$与样本特异性不确定性$\ ext{\hatσ}$。在合成显微图像基准上对比三种架构（SE-CNN、ResNet-50、VGG-16），ResNet-50达到$R^2 = 0.93$，RMSE = 0.18 log-cycles，宏平均F1 = 0.91。GNLL目标使期望校准误差相比均方误差基线降低76%（ECE：$0.089 \rightarrow 0.021$）。Grad-CAM热力图显示网络关注具有冶金学意义的微结构特征。单张图像推理时间低于65毫秒，管道与合成数据生成器已开源。由于验证完全依赖合成图像，结果证明方法在模拟条件下的合理性；下一步将在真实现场样本上开展域迁移研究。
原文摘要 · Abstract (English)
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.
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