journal article·2026-01

A new fatigue life prediction method for welded joints based on machine learning incorporating defect information and physics of failure

Mengqi Liu, Xiaogang Wang, Dong Mi, Zhicheng Liu, Xiangyun Long, Shengming Wen, Chao Jiang

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Fatigue and fracture mechanics · Non-Destructive Testing Techniques · Welding Techniques and Residual Stresses

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (73)

Show source-provided references
1. Ghosh · 2022
2. Fatigue strength assessment of welded joints incorporating the variability in local weld geometry using a probabilistic framework10.1016/j.ijfatigue.2022.107364 ↗
3. The potency of defects on fatigue of additively manufactured metals10.1016/j.ijmecsci.2022.107185 ↗
4. Fatigue from defect: Influence of size, Type, Position, Morphology and Loading10.1016/j.ijfatigue.2021.106531 ↗
5. Fatigue life estimation of additively manufactured Ti-6Al-4V: Sensitivity, scatter and defect description in Damage-tolerant models10.1016/j.actamat.2022.118189 ↗
6. Schijve · 2001 · 1
7. Defects in additive manufactured metals and their effect on fatigue performance: a state-of-the-art review10.1016/j.pmatsci.2020.100724 ↗
8. Anderson · 2005
9. Fatigue limit prediction model and fatigue crack growth mechanism for selective laser melting Ti6Al4V samples with inherent defects10.1016/j.ijfatigue.2020.106008 ↗
10. Effects of Small Defects and Nonmetallic Inclusions on the Fatigue Strength of Metals
11. Fatigue mechanism of medium-carbon steel welded joint: Competitive impacts of various defects10.1016/j.ijfatigue.2021.106363 ↗
12. Quantitative relationship between weld defect characteristic and fatigue crack initiation life for high-cycle fatigue property10.1016/j.ijfatigue.2019.02.028 ↗
13. Fatigue life evaluation of Ti–6Al–4V welded joints manufactured by electron beam melting10.1111/ffe.13490 ↗
14. A modified strain energy density exhaustion model for creep–fatigue life prediction10.1016/j.ijfatigue.2016.03.005 ↗
15. Assessment of the effect of isolated porosity defects on the fatigue performance of additive manufactured titanium alloy
16. Defect analysis and fatigue design basis for Ni-based superalloy 718 manufactured by selective laser melting10.1016/j.ijfatigue.2018.08.002 ↗
17. A crystal plasticity approach to understand fatigue response with respect to pores in additive manufactured aluminium alloys10.1016/j.ijfatigue.2022.106917 ↗
18. Effect of Lüders band on low cycle fatigue properties of a medium-Mn high strength steel10.1016/j.ijfatigue.2025.109132 ↗
19. Artificial intelligence: a powerful paradigm for scientific research10.1016/j.xinn.2021.100179 ↗
20. Machine learning and the physical sciences10.1103/RevModPhys.91.045002 ↗
21. A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction10.1016/j.ijfatigue.2022.106996 ↗
22. LBE corrosion fatigue life prediction of T91 steel and 316 SS using machine learning method assisted by symbol regression10.1016/j.ijfatigue.2023.107962 ↗
23. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach10.1016/j.ijfatigue.2022.107483 ↗
24. High-temperature high-cycle fatigue performance and machine learning-based fatigue life prediction of additively manufactured Hastelloy X10.1016/j.ijfatigue.2023.108012 ↗
25. Damage mechanics coupled with a transfer learning approach for the fatigue life prediction of bronze/steel diffusion welded bimetallic material10.1016/j.ijfatigue.2024.108631 ↗
26. A fatigue life prediction approach for laser-directed energy deposition titanium alloys by using support vector regression based on pore-induced failures10.1016/j.ijfatigue.2022.106748 ↗
27. Fatigue life prediction considering mean stress effect based on random forests and kernel extreme learning machine10.1016/j.ijfatigue.2022.106761 ↗
28. Estimation of fatigue life of welded structures incorporating importance analysis of influence factors: a data-driven approach10.1016/j.engfracmech.2023.109103 ↗
29. Impact of surface and pore characteristics on fatigue life of laser powder bed fusion Ti–6Al–4V alloy described by neural network models10.1038/s41598-021-99959-6 ↗
30. A physics-informed neural network approach for predicting fatigue life of SLM 316L stainless steel based on defect features10.1016/j.ijfatigue.2024.108486 ↗
31. Physics-guided machine learning frameworks for fatigue life prediction of AM materials10.1016/j.ijfatigue.2023.107658 ↗
32. A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace10.1016/j.ijfatigue.2023.107536 ↗
33. A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing10.1016/j.matdes.2022.111089 ↗
34. Uncertainty-aware fatigue-life prediction of additively manufactured Hastelloy X superalloy using a physics-informed probabilistic neural network10.1016/j.ress.2023.109852 ↗
35. Data-driven fatigue life prediction of small-deep holes in a nickel-based superalloy after a cold expansion process10.1016/j.ijfatigue.2024.108159 ↗
36. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network10.1016/j.ijfatigue.2022.107234 ↗
37. On the integration of domain knowledge and branching neural network for fatigue life prediction with small samples10.1016/j.ijfatigue.2023.107648 ↗
38. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures10.1016/j.engfracmech.2021.108130 ↗
39. Fatigue life prediction of cold expansion hole using physics-enhanced data-driven method10.1016/j.ijfatigue.2024.108634 ↗
40. Creep-fatigue reliability assessment for high-temperature components fusing on-line monitoring data and physics-of-failure by engineering damage mechanics approach10.1016/j.ijfatigue.2022.107481 ↗
41. Physics-informed machine learning for low-cycle fatigue life prediction of 316 stainless steels10.1016/j.ijfatigue.2024.108187 ↗
42. A physics-informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth10.1016/j.ijfatigue.2024.108678 ↗
43. Comparison of machine learning and stress concentration factors‐based fatigue failure prediction in small‐scale butt‐welded joints10.1111/ffe.13800 ↗
44. A modified physics-informed neural network to fatigue life prediction of deck-rib double-side welded joints10.1016/j.ijfatigue.2024.108566 ↗
45. Machine learning-based fatigue life prediction of double-sided U-rib welded joints considering multiple factors10.1016/j.ijfatigue.2025.109187 ↗
46. Fatigue life prediction for welding components based on hybrid intelligent technique10.1016/j.msea.2015.07.006 ↗
47. Evolution of microstructures and properties of the GH4169 superalloy during short-term and high-temperature processing10.1016/j.msea.2018.12.019 ↗
48. Novel fatigue life prediction method of a Ni-based superalloy welded joint considering defect and temperature10.1016/j.ijfatigue.2023.107924 ↗
49. Growth and stability of interacting surface flaws of arbitrary shape10.1016/0013-7944(83)90027-9 ↗
50. Effects of defects, inclusions and inhomogeneities on fatigue strength10.1016/0142-1123(94)90001-9 ↗
51. Murakami · 2019
52. Small crack growth model from low to very high cycle fatigue regime for internal fatigue failure of high strength steel10.1016/j.ijfatigue.2016.04.016 ↗
53. The dominant role of defects on fatigue behaviour of a SLM Ni-based superalloy at elevated temperature10.1016/j.ijfatigue.2023.107894 ↗
54. Macroscopically modeling fatigue life of additively manufactured metals: Pore-defect informed phase-field model10.1016/j.jmps.2024.106008 ↗
55. Influence of the defect size, type, and position on the High Cycle Fatigue behavior of Ti-6Al-4V processed by laser powder bed fusion10.1016/j.ijfatigue.2024.108783 ↗
56. A comprehensive survey on support vector machine classification: applications, challenges and trends10.1016/j.neucom.2019.10.118 ↗
57. Support-vector networks10.1023/A:1022627411411 ↗
58. A tutorial on support vector regression10.1023/B:STCO.0000035301.49549.88 ↗
59. James · 2013
60. Bishop · 2006
61. Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime10.1016/j.ijfatigue.2023.107645 ↗
62. Scikit-learn: Machine Learning in Python
63. Hyperparameter design criteria for support vector classifiers10.1016/S0925-2312(03)00430-2 ↗
64. Estimation of prediction error by using K-fold cross-validation10.1007/s11222-009-9153-8 ↗
65. S. Arlot, A. Celisse, A survey of cross-validation procedures for model selection, 2010.10.1214/09-SS054 ↗
66. R. Kohavi, A study of cross-validation and bootstrap for accuracy estimation and model selection, in: Ijcai, Montreal, Canada, 1995, pp. 1137-1145.
67. A guide to appropriate use of correlation coefficient in medical research
68. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models
69. A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys10.1016/j.engfracmech.2023.109714 ↗
70. Random Forests10.1023/A:1010933404324 ↗
71. A data-driven model for predicting fatigue performance of high-strength steel wires based on optimized XGBOOST10.1016/j.engfailanal.2024.108710 ↗
72. Approximating XGBoost with an interpretable decision tree10.1016/j.ins.2021.05.055 ↗
73. Bayesian optimization algorithm based support vector regression analysis for estimation of shear capacity of FRP reinforced concrete members10.1016/j.asoc.2021.107281 ↗