journal article·2025-01

Prediction of fatigue crack damage using in-situ scanning electron microscopy and machine learning

Jianli Zhou, Yixu Zhang, Ni Wang, Wenjie Gao, Ling’en Liu, Liang Tang, Jin Wang, Junxia Lu, Yuefei Zhang, Ze Zhang

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OpenAlex topics: Non-Destructive Testing Techniques · Infrastructure Maintenance and Monitoring · Industrial Vision Systems and Defect Detection

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References (44)

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1. Microstructural fatigue mechanisms: Cyclic slip irreversibility, crack initiation, non-linear elastic damage analysis[J]10.1016/j.ijfatigue.2012.06.007 ↗
2. Reed, R. C. The superalloys: fundamentals and applications. 2008.
3. The low-cycle fatigue deformation mechanisms of two single crystal superalloys at room temperature and 600° C[J]10.1016/j.scriptamat.2019.06.033 ↗
4. The behaviour of short fatigue cracks and their initiation part ii-a general summary[J]10.1111/j.1460-2695.1987.tb01153.x ↗
5. Schijve · Fatigue Struct Mater · 2001
6. In-situ SEM study of slip-controlled short-crack growth in single-crystal nickel superalloy[J]10.1016/j.msea.2018.11.040 ↗
7. Improved density peak clustering-based adaptive Gaussian mixture model for damage monitoring in aircraft structures under time-varying conditions[J]10.1016/j.ymssp.2019.01.034 ↗
8. Detection of a fatigue crack in a UH-60A planet gear carrier using vibration analysis[J]10.1016/j.ymssp.2006.05.010 ↗
9. Active Monitoring of Fatigue Crack in the Weld Zone of Bogie Frames Using Ultrasonic Guided Waves[J]10.3390/s19153372 ↗
10. Machine learning based features matching for fatigue crack detection[J]10.1016/j.procs.2020.06.063 ↗
11. A vision-based method for crack detection in gusset plate welded joints of steel bridges using deep convolutional neural networks[J]10.1016/j.autcon.2019.02.013 ↗
12. Deeper Networks for Pavement Crack Detection
13. An improved Otsu method using the weighted object variance for defect detection[J]10.1016/j.apsusc.2015.05.033 ↗
14. Joint Optimization of Crack Segmentation With an Adaptive Dynamic Threshold Module[J]
15. Detection for rail surface defects via partitioned edge feature[J]10.1109/TITS.2021.3058635 ↗
16. Research on the real-time detection method for image processing–Based civil structure crack[J]10.18280/ts.390638 ↗
17. A random forest based machine learning approach for mild steel defect diagnosis
18. Machine learning-based real-time visible fatigue crack growth detection[J]10.1016/j.dcan.2021.03.003 ↗
19. A novel deep learning approach of multiaxial fatigue life-prediction with a self-attention mechanism characterizing the effects of loading history and varying temperature[J]10.1016/j.ijfatigue.2022.106851 ↗
20. Fatigue modeling using neural networks: A comprehensive review[J]10.1111/ffe.13640 ↗
21. Deep learning[J]10.1038/nature14539 ↗
22. A deep learning-based approach for refined crack evaluation from shield tunnel lining images[J]10.1016/j.autcon.2021.103934 ↗
23. Machine vision-based surface crack analysis for transportation infrastructure[J]10.1016/j.autcon.2021.103973 ↗
24. Structural health monitoring by using a sparse coding-based deep learning algorithm with wireless sensor networks[J]10.1007/s00779-014-0800-5 ↗
25. Transfer Learning Methods for Fractographic Detection of Fatigue Crack Initiation in Additive Manufacturing[J]10.1109/ACCESS.2024.3350709 ↗
26. Increasing the robustness of material-specific deep learning models for crack detection across different materials[J]10.1016/j.engstruct.2019.110157 ↗
27. Segmentation of fatigue cracks in ancillary steel structures using deep learning convolutional neural networks
28. Strohmann, T., Starostin‐Penner, D., Breitbarth, E., & Requena, G. Automatic detection of fatigue crack paths using digital image correlation and convolutional neural networks[J]. Fatigue & Fracture of Engineering Materials & Structures. 2021.5, 44(5), 1336-1348.10.1111/ffe.13433 ↗
29. Deep learning approaches to semantic segmentation of fatigue cracking within cyclically loaded nickel superalloy[J]10.1016/j.commatsci.2021.110683 ↗
30. A Unified Mixed Deep Neural Network for Fatigue Damage Detection in Components with Different Stress Concentrations[J]10.3390/app13031542 ↗
31. Assessment of Transfer Learning Capabilities for Fatigue Damage Classification and Detection in Aluminum Specimens with Different Notch Geometries[J]10.3390/met12111849 ↗
32. Prediction and characterization of microstructure evolution based on deep learning method and in-situ scanning electron microscope[J]10.1016/j.matchar.2023.113230 ↗
33. Remaining fatigue life prediction of additively manufactured Inconel 718 alloy based on in-situ SEM and deep learning[J]
34. Development and application of a high-temperature imaging system for in-situ scanning electron microscope[J]
35. Stress intensity factor equations for branched crack growth[J]10.1016/j.engfracmech.2005.05.004 ↗
36. The effect of crack length and maximum stress on the fatigue crack growth rates of engineering alloys[J]10.1016/j.ijfatigue.2022.106919 ↗
37. Colom, M., Rodriguez-Aseguinolaza, J., Mendioroz, A., & Salazar, A. (2021). Imaging real cracks: evaluation of the depth and width of narrow fatigue cracks in Al-alloys using laser-spot lock-in thermography. Paper presented at the Thermosense: Thermal Infrared Applications XLIII.10.1117/12.2585024 ↗
38. Deep learning-based planar crack damage evaluation using convolutional neural networks[J]10.1016/j.engfracmech.2021.107604 ↗
39. Short fatigue crack parameters describing the lifetime of unnotched steel specimens[J]10.1016/S0142-1123(00)00101-8 ↗
40. A wind turbine damage detection algorithm designed based on YOLOv8[J]
41. In-situ SEM study on fatigue crack behavior of a nickel-based single crystal alloy at 950° C and 1050° C[J]10.1016/j.matchar.2023.112763 ↗
42. Tada · The stress analysis of cracks[J] · 19731973 · 34
43. A Novel Physics-Regularized Interpretable Machine Learning Model for Grain Growth[J]
44. Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks[J]