A machine learning-based approach to predict the fatigue life of three-dimensional cracked specimens
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OpenAlex topics: Fatigue and fracture mechanics · Non-Destructive Testing Techniques · Infrastructure Maintenance and Monitoring
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References (34)
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1. Ziqiang · 2009
2. C. E. Inglis, “Stresses in a Plate Due to Presence of Cracks and Sharp Corners,” trans inst naval archit, 1913. http://dx.doi.org/.
3. Analysis of stresses and strains near the end of a crack traversing a plate. J Appl Mech Trans ASME 24: 361–36410.1115/1.4011547 ↗
4. P. Paris, “A critical analysis of crack propagation laws,” J Basic Eng, vol. 85, 1963.10.1115/1.3656900 ↗
5. “Fatigue crack closure under cyclic tension,” (in English)10.1016/0013-7944(70)90028-7 ↗
6. Surface fatigue crack growth under variable amplitude loading10.1016/j.engfracmech.2020.107317 ↗
7. Application of the strip-yield model from the NASGRO software to predict fatigue crack growth in aluminium alloys under constant and variable amplitude loading10.1016/j.engfracmech.2006.06.014 ↗
8. Newman, “A crack-closure model for predicting fatigue crack growth under aircraft spectrum loading,” ASTM special technical publications, 1981. doi:http://dx.doi.org/.10.1520/STP28334S ↗
9. Equivalent thickness-based three dimensional stress fields and fatigue growth of part-through cracks emanating from a circular hole10.1016/j.engfracmech.2020.106927 ↗
10. A crack opening stress equation for fatigue crack growth10.1007/BF00020751 ↗
11. Elastoplastic three dimensional crack border field-II. Asymptotic solution for the field10.1016/0013-7944(93)90307-E ↗
12. Elasto-plastic three-dimensional crack border field-III. Fracture parameters10.1016/0013-7944(94)00215-4 ↗
13. Elastoplastic three dimensional crack border field-I. Singular structure of the field10.1016/0013-7944(93)90306-D ↗
14. Three-dimensional analyses of plastic constraint for through-thickness cracked bodies10.1016/S0013-7944(98)00102-7 ↗
15. A model for the through-thickness fatigue crack closure10.1016/S0013-7944(99)00055-7 ↗
16. Crevier · 1993
17. Zhou · 2016
18. A logical calculus of the ideas immanent in nervous activity10.1007/BF02478259 ↗
19. Neural networks and physical systems with emergent collective computational abilities.10.1073/pnas.79.8.2554 ↗
20. Learning Representations by Back Propagating Errors10.1038/323533a0 ↗
21. A. Graves and N. Jaitly, “Towards end-to-end speech recognition with recurrent neural networks,” 2014.
22. ImageNet Classification with Deep Convolutional Neural Networks10.1145/3065386 ↗
23. A Survey on Deep Learning in Medical Image Analysis10.1016/j.media.2017.07.005 ↗
24. Learning Deep Transformer Models for Machine Translation10.18653/v1/P19-1176 ↗
25. A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis10.1098/rsif.2017.0844 ↗
26. A machine learning approach to fracture mechanics problems10.1016/j.actamat.2020.03.016 ↗
27. Deep learning predicts path-dependent plasticity10.1073/pnas.1911815116 ↗
28. Astm · 2015
29. An empirical stress-intensity factor equation for the surface crack10.1016/0013-7944(81)90116-8 ↗
30. Equivalent thickness conception for corner cracks10.1016/j.ijsolstr.2010.04.012 ↗
31. An equivalent thickness conception for evaluation of corner and surface fatigue crack closure10.1016/j.engfracmech.2012.12.013 ↗
32. Crack opening stress measurements of surface cracks in 7075–T6 aluminium alloy plate specimen through electron fractography10.1111/j.1460-2695.1992.tb01274.x ↗
33. A Survey of Deep Active Learning10.1145/3472291 ↗
34. Liu X , Athanasiou C E , Padture N P , et al. Knowledge extraction and transfer in data-driven fracture mechanics. Proceedings of the National Academy of Sciences, 2021, 118(23):e2104765118. 10.1073/pnas.2104765118.10.1073/pnas.2104765118 ↗