journal article·2026-03

Physics-informed transformer model for multiaxial ratchetting-fatigue life prediction of SUS301L stainless steel butt-welded joint

Lvfeng Jiang, Huiliang Luo, Yuxuan Liu, Yanan Hu, Hui Li, Xuejiao Shao, Xu Zhang, Qianhua Kan, Guozheng Kang

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OpenAlex topics: High Temperature Alloys and Creep · Fatigue and fracture mechanics · Ultrasonics and Acoustic Wave Propagation

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References (77)

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1. Review on fatigue life prediction models of welded joint10.1007/s10409-020-00957-0 ↗
2. Isothermal and thermomechanical fatigue behaviour of type 316LN austenitic stainless steel base metal and weld joint10.1016/j.msea.2019.138627 ↗
3. Fatigue experiment and assessment of butt-welded joints in steel structures considering misalignment and weld geometry effects10.1016/j.ijfatigue.2024.108200 ↗
4. Influence of material model on prediction accuracy of welding residual stress in an austenitic stainless steel multi-pass butt-welded joint10.1007/s11665-017-2626-6 ↗
5. FEM analysis of residual stress induced by repair welding in SUS304 stainless steel pipe butt-welded joint10.1016/j.jmapro.2020.09.006 ↗
6. Experimental study on ratchetting-fatigue interaction of SS304 stainless steel in uniaxial cyclic stressing10.1016/j.msea.2006.07.006 ↗
7. Multiaxial ratchetting–fatigue interactions of annealed and tempered 42CrMo steels: Experimental observations10.1016/j.ijfatigue.2008.06.002 ↗
8. Uniaxial ratcheting behaviour of 304L stainless steel and ER308L weld joints10.1016/j.msea.2017.09.109 ↗
9. Experimental study on multiaxial ratchetting-fatigue interaction of SUS301L stainless steel tubular welded joint10.1016/j.ijfatigue.2024.108411 ↗
10. Fatigue damage assessment of various welded joints under uniaxial loading based on energy methods10.1016/j.ijfatigue.2010.09.019 ↗
11. Advances in fatigue life modeling: a review10.1016/j.rser.2017.09.047 ↗
12. Multiaxial fatigue assessment of welded joints using the notch stress approach10.1016/j.ijfatigue.2015.10.021 ↗
13. An equivalent stress parameter for multi-axial fatigue evaluation of welded components including non-proportional loading effects10.1016/j.ijfatigue.2017.01.006 ↗
14. Multiaxial fatigue assessment of friction stir welded tubular joints of Al 6082-T610.1016/j.ijfatigue.2016.08.010 ↗
15. Notch stress to assess multiaxial fatigue of complex welded structures under non-proportional loading10.1016/j.tafmec.2019.04.014 ↗
16. A novel elastic strain energy density approach for fatigue evaluation of welded components10.1016/j.engfracmech.2023.109713 ↗
17. A moment of load path-based parameter for modeling multiaxial fatigue damage of welded structures10.1016/j.ijfatigue.2023.107575 ↗
18. A structural strain method for low-cycle fatigue evaluation of welded components10.1016/j.ijpvp.2014.03.003 ↗
19. Structural strain approach to assess thermo-mechanical fatigue of thin-walled welded joints10.1016/j.ijfatigue.2020.105722 ↗
20. Fatigue strength assessment of load-carrying cruciform joints with material mismatching in low- and high-cycle fatigue regions based on the effective notch concept10.1016/j.ijfatigue.2011.12.016 ↗
21. Low cycle fatigue life assessment of welded high-strength structural steels based on nominal and local design concepts10.1016/j.ijfatigue.2017.02.014 ↗
22. Notch energy-based low and high cycle fatigue assessment of load-carrying cruciform welded joints considering the strength mismatch10.1016/j.ijfatigue.2021.106410 ↗
23. Synthetical effect of material inhomogeneity and welding defects on fatigue behavior of 2205 duplex stainless steel cruciform welded Joints: Experiments and Life-prediction model10.1016/j.ijfatigue.2022.107472 ↗
24. Low‐cycle fatigue of 1Cr–18Ni–9Ti stainless steel and related weld metal under axial, torsional and 90° out‐of‐phase loading10.1111/j.1460-2695.2004.00740.x ↗
25. Fatigue analysis of multiaxially loaded crane runway structures including welding residual stress effects10.1016/j.ijfatigue.2015.04.009 ↗
26. Low-cycle multiaxial fatigue behavior and life prediction of Q235B steel welded material10.1016/j.ijfatigue.2019.06.027 ↗
27. Experimental investigation and low-cycle fatigue life prediction of welded Q355B steel10.1016/j.jcsr.2020.106497 ↗
28. High cycle fatigue analysis in presence of residual stresses by using a continuum damage mechanics model10.1016/j.ijfatigue.2014.08.013 ↗
29. Fatigue life prediction for butt‐welded joints considering weld‐induced residual stresses and initial damage, relaxation of residual stress, and elasto‐plastic fatigue damage10.1111/ffe.12993 ↗
30. Fatigue damage evolution and lifetime prediction of welded joints with the consideration of residual stresses and porosity10.1016/j.ijfatigue.2017.06.014 ↗
31. Fatigue life of a dissimilar welded joint considering the weld residual stress: experimental and finite element simulation10.1016/j.ijfatigue.2018.01.002 ↗
32. Quantitative thermography for fatigue damage assessment and life prediction of welded components10.1016/j.mechmat.2021.104120 ↗
33. The influence of heterogeneous microstructure on low-cycle fatigue behavior in NiCrMoV welded joint10.1007/s11665-022-06809-1 ↗
34. Study of the effect of microstructural variation on the low cycle fatigue behavior of laser welded DP600 steel: Simulation and experimental validation10.1016/j.msea.2018.06.004 ↗
35. Investigation of the High-Temperature Low-Cycle fatigue failure characteristics of P91 steel weld joints and their fatigue strength reduction factors under various load control regimes10.1016/j.ijfatigue.2023.108085 ↗
36. Experimental and numerical study on ultra low cycle fatigue fracture of X steel tubular joints with CHS braces to SHS chord10.1016/j.tws.2021.107564 ↗
37. Cyclic plasticity of welded P91 material for simple and complex power plant connections10.1016/j.ijfatigue.2016.02.005 ↗
38. A semi-empirical life-prediction model for multiaxial ratchetting-fatigue interaction of SUS301L stainless steel tubular welded joint10.1016/j.ijfatigue.2024.108538 ↗
39. Numerical study on the ratcheting performance of rail flash butt welds in heavy haul operations10.1016/j.ijmecsci.2021.106434 ↗
40. A low-cycle fatigue life-prediction model for SUS301L stainless steel butt-welded joint with considering ratchetting10.1016/j.ijfatigue.2020.105777 ↗
41. Machine-learning-based investigation into the effect of defect/inclusion on fatigue behavior in steels10.1016/j.ijfatigue.2021.106597 ↗
42. Kraus MA, Bartsch H. Discovery of fatigue strength models via feature engineering and automated eXplainable machine learning applied to the welded transverse stiffener 2025. doi: 10.48550/ARXIV.2507.02005.10.1016/j.ijfatigue.2025.109324 ↗
43. An entropy-based neighborhood rough set and PSO-SVRM model for fatigue life prediction of titanium alloy welded joints10.3390/e21020117 ↗
44. Fatigue life prediction of welded joints based on improved support vector regression model under two‐level loading10.1111/ffe.13969 ↗
45. Comparison of machine learning and stress concentration factors‐based fatigue failure prediction in small‐scale butt‐welded joints10.1111/ffe.13800 ↗
46. Finite element analysis results visualization of manual metal arc welding using an interactive mixed reality-based user interface10.1016/j.jmapro.2023.03.001 ↗
47. Artificial neural network based fatigue life assessment of friction stir welding AA2024-T351 aluminum alloy and multi-objective optimization of welding parameters10.1016/j.ijfatigue.2022.106840 ↗
48. Study on fatigue life of high-strength steel rebars joined by flash butt welding based on experimental and machine learning approaches10.1016/j.engfailanal.2023.107812 ↗
49. A vision-based method for lap weld defects monitoring of galvanized steel sheets using convolutional neural network10.1016/j.jmapro.2020.12.067 ↗
50. A CNN-based lamb wave processing model for field monitoring of fatigue cracks in orthotropic steel bridge decks10.1016/j.istruc.2023.105146 ↗
51. Attention-based multi-fidelity deep neural network for efficient estimation of welding residual stresses in V-shaped butt-welded high strength steel plate10.1016/j.eswa.2024.126137 ↗
52. Prediction of fatigue failure in small-scale butt-welded joints with explainable machine learning
53. Estimation of fatigue life of welded structures incorporating importance analysis of influence factors: A data-driven approach10.1016/j.engfracmech.2023.109103 ↗
54. A physics-informed deep learning approach for combined cycle fatigue life prediction10.1016/j.jcsr.2024.109000 ↗
55. A survey of convolutional neural networks: analysis, applications, and prospects10.1109/TNNLS.2021.3084827 ↗
56. A perspective on explainable artificial intelligence methods: SHAP and LIME10.1002/aisy.202400304 ↗
57. A comparison of uniaxial and multiaxial non-proportional fatigue properties in cast Al-Si-Cu-T6 alloys solidified at two cooling rates: Fatigue behavior, fracture characteristics and dislocation evolution10.1016/j.matchar.2022.111957 ↗
58. High cycle fatigue properties, failure mechanisms and microstructure evolution of 9Cr3W3Co steel under different stress ratios at 650 °C10.1016/j.ijfatigue.2025.108971 ↗
59. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, et al. Attention is all you need; 2017. doi: 10.48550/ARXIV.1706.03762.
60. Deep residual learning for image recognition
61. Boya SK, Subramani D. A physics-informed transformer neural operator for learning generalized solutions of initial boundary value problems, 2024. doi: 10.48550/ARXIV.2412.09009.
62. Physics informed neural networks for fluid flow analysis with repetitive parameter initialization10.1038/s41598-025-99354-5 ↗
63. Wang Y, Liu S, Lv S, Liu G. Meta-learning and knowledge discovery based physics-informed neural network for remaining useful life prediction, 2025. doi: 10.48550/ARXIV.2504.13797.
64. Raissi M, Perdikaris P, Karniadakis GE. Physics informed deep learning (Part I): data-driven solutions of nonlinear partial differential equations, 2017. doi: 10.48550/ARXIV.1711.10561.
65. Physics-informed learning of governing equations from scarce data10.1038/s41467-021-26434-1 ↗
66. A defect-based physics-informed machine learning framework for fatigue finite life prediction in additive manufacturing10.1016/j.matdes.2022.111089 ↗
67. A physics-informed neural network approach to fatigue life prediction using small quantity of samples10.1016/j.ijfatigue.2022.107270 ↗
68. Using physics-informed neural networks with small datasets to predict the length of gas turbine nozzle cracks10.1016/j.aei.2023.102232 ↗
69. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations10.1016/j.jcp.2018.10.045 ↗
70. Daw A, Karpatne A, Watkins W, Read J, Kumar V. Physics-guided neural networks (PGNN): an application in lake temperature modelling, 2017. doi: 10.48550/ARXIV.1710.11431.
71. Daw A, Thomas RQ, Carey CC, Read JS, Appling AP, Karpatne A. Physics-Guided architecture (PGA) of neural networks for quantifying uncertainty in lake temperature modelling, 2019. doi: 10.48550/ARXIV.1911.02682.10.1137/1.9781611976236.60 ↗
72. Physics guided neural networks for modelling of non-linear dynamics10.1016/j.neunet.2022.07.023 ↗
73. A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace10.1016/j.ijfatigue.2023.107536 ↗
74. Predicting the onset of void swelling in irradiated metals with machine learning10.1016/j.jnucmat.2019.05.054 ↗
75. Prediction of fatigue crack propagation behavior in elastic plastic region under block loading for type 316 steel via artificial neural network approach10.1016/j.ijfatigue.2024.108725 ↗
76. Stress-based fatigue failure models for uniaxial ratchetting–fatigue interaction10.1016/j.ijfatigue.2007.08.005 ↗
77. Rate-dependent multiaxial life prediction for polyamide-6 considering ratchetting: Semi-empirical and physics-informed machine learning models10.1016/j.ijfatigue.2022.107086 ↗