Model Releases
Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning
arXiv:2607.28695v1 Announce Type: cross Abstract: 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
arXiv:2607.28695v1 Announce Type: cross Abstract: 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 hat{sigma}.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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Source: arXiv cs.AI | 2026-08-03