Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning

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 FATIGUECV, a computer-vision framework estimating the fatigue life (log10(Nf)) 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 log10(Nf) and sample-specific uncertainty ˆσ. Evaluating three architectures (SE-CNN, ResNet-50, VGG-16) on a synthetic micrograph benchmark, ResNet-50 achieves R2 = 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→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.