{"slug": "predicting-steel-fatigue-life-from-micrographs-using-physics-informed-deep", "title": "Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning", "summary": "Researchers at an undisclosed institution present CV, a computer vision framework that estimates the fatigue life of lightweight alloy steels from optical micrographs, achieving an R² of 0.93 and RMSE of 0.18 log-cycles with ResNet-50 on a synthetic benchmark. The physics-informed pipeline, which uses a 28-dimensional feature extractor and a CNN with Gaussian negative log-likelihood loss, reduces Expected Calibration Error by 76% compared to a mean-squared-error baseline. The method runs in under 65 ms per image and is open-sourced, though validation is currently limited to synthetic micrographs.", "body_md": "arXiv:2607.28695v1 Announce Type: new\nAbstract: 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.", "url": "https://wpnews.pro/news/predicting-steel-fatigue-life-from-micrographs-using-physics-informed-deep", "canonical_source": "https://arxiv.org/abs/2607.28695", "published_at": "2026-08-03 04:00:00+00:00", "updated_at": "2026-08-03 04:02:45.424542+00:00", "lang": "en", "topics": ["computer-vision", "machine-learning", "artificial-intelligence"], "entities": ["CV", "ResNet-50", "OpenCV"], "alternates": {"html": "https://wpnews.pro/news/predicting-steel-fatigue-life-from-micrographs-using-physics-informed-deep", "markdown": "https://wpnews.pro/news/predicting-steel-fatigue-life-from-micrographs-using-physics-informed-deep.md", "text": "https://wpnews.pro/news/predicting-steel-fatigue-life-from-micrographs-using-physics-informed-deep.txt", "jsonld": "https://wpnews.pro/news/predicting-steel-fatigue-life-from-micrographs-using-physics-informed-deep.jsonld"}}