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

• URL: http://arxiv.org/abs/2607.28695v1
• Date: Thu, 30 Jul 2026 10:13:21 GMT
• Title: Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning
• Abstract summary: 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.<n>Running in under 65 ms per image, the pipeline and synthetic dataset generator are open-sourced.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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 $\hatσ$.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.

Related papers

• FPicker: Topology-Guided Evolution for Filament Tracing in Low-SNR Microscopy [14.980713248913732]
  FPicker is a topology-guided framework for 3D filament tracing in Cryo-Electron Microscopy.<n>It unifies perception via a center-endpoint representation and an open-curve evolution module to explicitly model non-cyclic connectivity.<n>Its zero-shot performance on the real-world EMPIAR dataset exhibits robust topological resistance, achieving a state-of-the-art 82.9% mSAP upon fine-tuning.
  arXiv  Detail & Related papers  (2026-09-08T06:25:44Z)
• When Less Is More: Simplicity Beats Complexity for Physics-Constrained InSAR Phase Unwrapping [0.34376560669160394]
  Operational phase unwrapping is the primary computational bottleneck in InSAR-based volcanic and seismic monitoring.<n>We present the first large-scale architectural ablation study on a global LiCSAR benchmark (20 frames, 39,724 patches, 651M pixels)
  arXiv  Detail & Related papers  (2026-04-28T02:36:08Z)
• Thermal Anomaly Detection using Physics Aware Neuromorphic Networks: Comparison between Raw and L1C Sentinel-2 Data [5.904895437536006]
  Recent Earth Observation approaches have shown that thermal anomaly detection can be performed directly on decompressed Level-0 (L0) sensor data.<n>Direct exploitation of raw data remains challenging due to domain shift, sensor drift, radiometric inconsistencies, and the scarcity of labelled training samples.<n>This work proposes a Physics-Aware Neuromorphic Network framework for onboard thermal anomaly detection.
  arXiv  Detail & Related papers  (2026-04-10T02:15:02Z)
• Sketch2Feedback: Grammar-in-the-Loop Framework for Rubric-Aligned Feedback on Student STEM Diagrams [0.0]
  We present Sketch2Feedback, a grammar-in-the-loop framework that decomposes the problem into four stages.<n>We evaluate on two synthetic micro-benchmarks, FBD-10 and Circuit-10, each with 500 images spanning standard and hard noise augmentation tiers.
  arXiv  Detail & Related papers  (2026-02-19T20:34:24Z)
• A Physics-Constrained, Design-Driven Methodology for Defect Dataset Generation in Optical Lithography [1.0610015128259989]
  This study proposes a novel methodology for generating large-scale, physically valid defect datasets with pixel-level annotations.<n>We constructed a comprehensive dataset of 3,530 Optical micrographs containing 13,365 annotated defect instances.
  arXiv  Detail & Related papers  (2025-12-09T06:13:33Z)
• Exploring the Hierarchical Reasoning Model for Small Natural-Image Classification Without Augmentation [51.56484100374058]
  It is evaluated on MNIST, CIFAR-10, and CIFAR-100 under a deliberately raw regime.<n>It is concluded that, for small-resolution image classification without augmentation, HRM is not competitive with even simple convolutional architectures.
  arXiv  Detail & Related papers  (2025-10-04T01:22:41Z)
• Model Discovery and Graph Simulation: A Lightweight Gateway to Chaos Engineering [0.0]
  Chaos engineering reveals resilience risks but is expensive and operationally risky to run broadly and often.<n>We claim that a simple connectivity-only topological model can provide fast, low-risk availability estimates under fail-stop faults.
  arXiv  Detail & Related papers  (2025-06-12T10:59:28Z)
• Simple ReFlow: Improved Techniques for Fast Flow Models [68.32300636049008]
  Diffusion and flow-matching models achieve remarkable generative performance but at the cost of many sampling steps. We propose seven improvements for training dynamics, learning and inference. We achieve state-of-the-art FID scores (without / with guidance, resp.) for fast generation via neural ODEs.
  arXiv  Detail & Related papers  (2024-10-10T11:00:55Z)
• Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler [103.97487121678276]
  Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs. We propose a novel Knowledge-driven Differential Filter Sampler(KDFS) with Masked Filter Modeling(MFM) framework for filter pruning.
  arXiv  Detail & Related papers  (2023-07-01T02:28:41Z)
• CrAM: A Compression-Aware Minimizer [103.29159003723815]
  We propose a new compression-aware minimizer dubbed CrAM that modifies the optimization step in a principled way. CrAM produces dense models that can be more accurate than the standard SGD/Adam-based baselines, but which are stable under weight pruning. CrAM can produce sparse models which perform well for transfer learning, and it also works for semi-structured 2:4 pruning patterns supported by GPU hardware.
  arXiv  Detail & Related papers  (2022-07-28T16:13:28Z)
• NeFSAC: Neurally Filtered Minimal Samples [90.55214606751453]
  NeFSAC is an efficient algorithm for neural filtering of motion-inconsistent and poorly-conditioned minimal samples. NeFSAC can be plugged into any existing RANSAC-based pipeline. We tested NeFSAC on more than 100k image pairs from three publicly available real-world datasets.
  arXiv  Detail & Related papers  (2022-07-16T08:02:05Z)
• Deep learning for gravitational-wave data analysis: A resampling white-box approach [62.997667081978825]
  We apply Convolutional Neural Networks (CNNs) to detect gravitational wave (GW) signals of compact binary coalescences, using single-interferometer data from LIGO detectors. CNNs were quite precise to detect noise but not sensitive enough to recall GW signals, meaning that CNNs are better for noise reduction than generation of GW triggers.
  arXiv  Detail & Related papers  (2020-09-09T03:28:57Z)
• Limited Angle Tomography for Transmission X-Ray Microscopy Using Deep Learning [12.991428974915795]
  Deep learning is applied to limited angle reconstruction in X-ray microscopy for the first time. The U-Net, the state-of-the-art neural network in biomedical imaging, is trained from synthetic data. The proposed method remarkably improves the 3-D visualization of the subcellular structures in the chlorella cell.
  arXiv  Detail & Related papers  (2020-01-08T12:11:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.