Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material
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OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals · Welding Techniques and Residual Stresses · Non-Destructive Testing Techniques
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References (41)
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1. Diffusion behavior at void tip and its contributions to void shrinkage during solid-state bonding10.1016/j.jmst.2017.12.001 ↗
2. Microstructure evolution of diffusion welded 304L/Zircaloy4 with copper interlayer10.1016/j.matdes.2016.12.008 ↗
3. Effect of bonding pressure on microstructure and mechanical properties of Ti-6Al-4V diffusion-bonded joint10.1007/s40194-016-0390-0 ↗
4. Bonding pressure effects on characteristics of microstructure, mechanical properties, and mass diffusivity of Ti-6Al-4V and TiAlNb diffusion-bonded joints10.1007/s40194-020-00989-x ↗
5. Solid-state diffusion joining of Ti6Al4V parts produced by selective laser melting: joint characteristics and bonding mechanism10.1007/s00170-021-07254-5 ↗
6. Dissimilar diffusion bonding behavior of hydrogenated Ti2AlNb-based and Ti-6Al-4V alloys10.1016/j.matdes.2018.08.034 ↗
7. Microstructure and mechanical properties of vacuum diffusion bonding joints for γ-TiAl based alloy10.1016/j.vacuum.2018.01.035 ↗
8. Mo De-feng, Song Ting-feng, Fang Yong-jian, Jiang Xiao-song, Luo Charles Q., Simpson Machael D., et al. A review on diffusion bonding between titanium alloys and stainless steels. Adv Mater Sci Eng 2018;2018:8701890. Doi: 10.1155/2018/8701890.10.1155/2018/8701890 ↗
9. Microstructure, microhardness and tribological properties of bronze-steel bimetallic composite produced by vacuum diffusion welding10.3390/ma15041588 ↗
10. A fatigue assessment of dissimilar titanium alloy diffusion bonds10.1016/j.ijfatigue.2004.11.004 ↗
11. Very high cycle fatigue behavior of dissimilar martensitic stainless-steel diffusion-bonded joints10.1166/mex.2019.1590 ↗
12. A continuum damage mechanics model for high cycle fatigue10.1016/S0142-1123(98)00005-X ↗
13. An investigation into various failure criteria on rolling contact fatigue through an improved probabilistic model10.1016/j.triboint.2023.108875 ↗
14. A microdamage model for FCC single crystals considering a mixed failure mechanism of slip and cleavage
15. Cui Li, Su Yin. Contact fatigue life prediction of rolling bearing considering machined surface integrity. Ind Lubr Tribol 2021;74(1):73–80. Doi: 10.1108/ILT-08-2021-0345.10.1108/ILT-08-2021-0345 ↗
16. Li Wanjia, Meng Xiangxu, Shu Kun, Zheng Dezhi, Wang Tingjian, Gu Le, et al. Effect of dented residual stress on the surface-defect-induced pitting under rolling contact loading of M50 bearing steel. Tribol Int 2024;194:109492. Doi: 10.1016/j.triboint.2024.109492.10.1016/j.triboint.2024.109492 ↗
17. Continuum damage mechanics-based approach to the fatigue life prediction for 7050–T7451 aluminum alloy with impact pit10.1177/1056789515608232 ↗
18. A new combined impact fatigue damage model and its application of influencing factors analysis on impact fatigue of TC18 titanium alloy
19. Continuum damage mechanics-based fatigue life prediction of L-PBF Ti-6Al-4V
20. Meso-scale damage mechanics modeling for high cycle fatigue behavior of additively manufactured components
21. Damage related material constants in continuum damage mechanics for unidirectional composites with matrix cracks10.1177/1056789518783239 ↗
22. Sun Bin, Xu Zhao-Dong. A continuum damage-based three-dimensional fracture simulation method for brittle-like materials. Int J DAMAGE Mech 2022;31(4):508–31. Doi: 10.1177/10567895211045116.10.1177/10567895211045116 ↗
23. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science
24. Fatigue modeling using neural networks: a comprehensive review10.1111/ffe.13640 ↗
25. Recent developments and future trends in fatigue life assessment of additively manufactured metals with particular emphasis on machine learning modeling10.1111/ffe.14152 ↗
26. Machine learning-based fatigue life prediction of metal materials: perspectives of physics-informed and data-driven hybrid methods
27. Pore-induced fatigue failure: a prior progressive fatigue life prediction framework of laser-directed energy deposition Ti-6Al-4V based on machine learning
28. A novel damage mechanics and XGBoost based approach for HCF life prediction of cast magnesium alloy considering internal defect characteristics
29. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network
30. Zhang Yongchao. Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing 2023. Doi: 10.1016/j.ress.2023.109186.10.1016/j.ress.2023.109186 ↗
31. A review of vibration-based gear wear monitoring and prediction techniques
32. Digital twin-driven intelligent assessment of gear surface degradation
33. Pan Sinno Jialin, Yang Qiang. A Survey on Transfer Learning. Ieee Trans Knowl Data Eng 2010;22(10):1345–59. Doi: 10.1109/TKDE.2009.191.10.1109/TKDE.2009.191 ↗
34. Xu Pengcheng, Ji Xiaobo, Li Minjie, Lu Wencong. Small data machine learning in materials science. NPJ Comput Mater 2023;9(1):42. Doi: 10.1038/s41524-023-01000-z.10.1038/s41524-023-01000-z ↗
35. Gear contact fatigue life prediction based on transfer learning
36. Wei Xiaolu, Zhang Chi, Han Siyu, Jia Zixi, Wang Chenchong, Xu Wei. High cycle fatigue S-N curve prediction of steels based on transfer learning guided long short term memory network. Int J Fatigue 2022;163:107050. Doi: 10.1016/j.ijfatigue.2022.107050.10.1016/j.ijfatigue.2022.107050 ↗
37. A transfer learning-based approach to fatigue life prediction of corroded bimetallic steel bars using small samples
38. Stepwise S-N curve and fish-eye failure in gigacycle fatigue10.1046/j.1460-2695.1999.00206.x ↗
39. A simple and efficient multiaxial fatigue damage model for engineering applications of macro-crack initiation10.1115/1.2904232 ↗
40. The nature and the mechanism of crack initiation and early growth for very-high-cycle fatigue of metallic materials – an overview
41. Gui Xiaolu, Gao Guhui, An Baifeng, Misra R.D.K., Bai Bingzhe. Relationship between non-inclusion induced crack initiation and microstructure on fatigue behavior of bainite/martensite steel in high cycle fatigue/very high cycle (HCF/VHCF) regime. Mater Sci Eng A 2021;803:140692. Doi: 10.1016/j.msea.2020.140692.10.1016/j.msea.2020.140692 ↗