journal article·2023-03

A hierarchical mechanism-informed neural network approach for assessing fretting fatigue of dovetail joints

Yujin Liu, Huang Yuan

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Abstract

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OpenAlex topics: Mechanical stress and fatigue analysis · Gear and Bearing Dynamics Analysis · Fatigue and fracture mechanics

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References (53)

Show source-provided references
1. The endurance of metals10.1243/PIME_PROC_1911_081_017_02 ↗
2. Zhou · 2001
3. Fretting corrosion and fatigue strength: Brief results of preliminary experiments10.1243/PIME_PROC_1941_146_012_02 ↗
4. Fracture mechanics assessment of stress concentrations in incomplete fretting contacts10.1016/j.engfracmech.2009.07.004 ↗
5. Computational analysis and characterization of fretting stress fields10.1016/j.commatsci.2008.06.020 ↗
6. A finite element-based approach to simulating the effects of debris on fretting wear10.1016/j.wear.2006.12.056 ↗
7. A dynamical FEA fretting wear modeling taking into account the evolution of debris layer10.1016/j.wear.2018.07.018 ↗
8. Modeling adhesive and abrasive wear phenomena in fretting interfaces: A multiphysics approach coupling friction energy, third body and contact oxygenation concepts10.1016/j.triboint.2021.107077 ↗
9. Modeling of fretting wear evolution in rough circular contacts in partial slip10.1016/j.ijmecsci.2006.08.021 ↗
10. An evaluation of the competition between wear and crack initiation in fretting conditions for Ti-6Al-4V alloy10.1016/j.wear.2015.03.010 ↗
11. Finite element analysis of fretting wear under variable coefficient of friction and different contact regimes10.1016/j.triboint.2016.11.044 ↗
12. Mechanics of fretting fatigue-Oxford’s contribution10.1016/j.triboint.2013.09.015 ↗
13. Fundamental investigations of fretting fatigue: Part 2, fretting fatigue testing machine and some test results10.1299/kikai1938.34.1183 ↗
14. The effect of rapidly varying contact stress fields on fretting fatigue10.1016/S0142-1123(01)00191-8 ↗
15. Fretting fatigue: Advances in basic understanding and applications
16. A multi-axial stress-based critical distance methodology to estimate fretting fatigue life10.1016/j.triboint.2016.07.028 ↗
17. A stress gradient approach for fretting fatigue assessment of metallic structural components10.1016/j.ijfatigue.2017.04.004 ↗
18. Structural integrity assessment of metallic components under multi-axial fatigue: the C-S criterion and its evolution10.1111/ffe.12037 ↗
19. Early fretting crack orientation by using the critical plane approach10.1016/j.ijfatigue.2018.04.015 ↗
20. Fretting-fatigue analysis of shot-peened aluminum and titanium test specimens10.1111/ffe.13367 ↗
21. Research on fretting fatigue life estimation model considering plastic effect
22. Experimental and computational investigation of fretting fatigue characterized by nominal contact pressure in dovetail structures
23. Experimental and computational investigations on fretting fatigue crack growth in dovetail joints10.2514/1.J061480 ↗
24. Experimental and computational investigations of nonlinear frictional behavior in threaded fasteners10.1016/j.triboint.2020.106737 ↗
25. Fatigue modeling using neural networks: A comprehensive review10.1111/ffe.13640 ↗
26. A novel approach based on the elastoplastic fatigue damage and machine learning models for life prediction of aerospace alloy parts fabricated by additive manufacturing10.1016/j.ijfatigue.2020.106089 ↗
27. Machine learning-based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L10.1016/j.ijfatigue.2020.105941 ↗
28. Fatigue behavior prediction and analysis of shot-peened mild carbon steels10.1016/j.ijfatigue.2018.06.004 ↗
29. Artificial intelligence assisted fatigue failure prediction10.1016/j.ijfatigue.2021.106580 ↗
30. A machine-learning fatigue life prediction approach of additively manufactured metals10.1016/j.engfracmech.2020.107508 ↗
31. Machine learning-based genetic feature identification and fatigue life prediction10.1111/ffe.13532 ↗
32. Fatigue life prediction of metallic materials considering mean stress effects by means of an artificial neural network10.1016/j.ijfatigue.2020.105527 ↗
33. A novel method of multi-axial fatigue life prediction based on deep learning10.1016/j.ijfatigue.2021.106356 ↗
34. A pattern recognition artificial neural network method for random fatigue loading life prediction10.1016/j.ijfatigue.2017.02.003 ↗
35. A machine learning approach to the prediction of fretting fatigue life10.1016/j.triboint.2019.105913 ↗
36. Machine learning predicts fretting and fatigue key mechanical properties
37. Determination of S−N curves with the application of artificial neural networks
38. Machine learning-based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting10.1016/j.ijfatigue.2022.106764 ↗
39. Fatigue life prediction of aluminum alloy via knowledge-based machine learning10.1016/j.ijfatigue.2021.106716 ↗
40. Socie · 2000
41. Cyclic plasticity modeling of nickel-based superalloy Inconel 718 under multi-axial thermomechanical fatigue loading conditions10.1016/j.ijfatigue.2018.09.017 ↗
42. A review of some plasticity and viscoplasticity constitutive theories10.1016/j.ijplas.2008.03.009 ↗
43. Yuan · 2002
44. Cyclic plasticity modeling and fatigue life assessment of the recasting material of a nickel-based superalloy induced by laser manufacturing
45. Multiscale plasticity behavior and fatigue performance of laser melting multi-layer nickel-based superalloys upon heat treatments10.1016/j.ijplas.2022.103404 ↗
46. Multi-axial fatigue life assessment of additively manufactured nickel-based superalloys10.1016/j.ijfatigue.2022.107049 ↗
47. A critical plane approach to multi-axial fatigue damage including out-of-plane loading10.1111/j.1460-2695.1988.tb01169.x ↗
48. Liu K. Method based on virtual strain–energy parameters for multi-axial fatigue life prediction ASTM STP 1191 1993;1191:67-84.10.1520/STP24796S ↗
49. Multiaxial fatigue damage parameter and life prediction under low cycle loading for GH4169 alloy and other structural materials10.1016/j.ijfatigue.2009.12.007 ↗
50. Prediction of fatigue lifetime under multi-axial cyclic loading using finite element analysis10.1016/j.matdes.2009.06.046 ↗
51. Correlation between elastic–plastic mechanical property and microstructural fabric tensors of dual-phase materials with the help of artificial neural network
52. A new cyclical generative adversarial network based data augmentation method for multi-axial fatigue life prediction10.1016/j.ijfatigue.2022.106996 ↗
53. Recent advances on notch effects in metal fatigue: A review10.1111/ffe.13195 ↗