journal article·2025-01

Probabilistic framework for strain-based fatigue life prediction and uncertainty quantification using interpretable machine learning

Xi Deng, Shun-Peng Zhu, Lanyi Wang, Changqi Luo, Sicheng Fu, Qingyuan Wang

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 · Metallurgy and Material Forming

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

References (71)

Show source-provided references
1. A review on fatigue life prediction methods for metals10.1155/2016/9573524 ↗
2. Low cycle fatigue life prediction—a new model10.1016/S0142-1123(96)00065-5 ↗
3. A state-of-the-art review on fatigue life prediction methods for metal structures10.1007/s007730200012 ↗
4. The exponential law of endurance tests
5. Weighted error criterion to evaluate strain-fatigue life prediction methods10.1016/j.ijfatigue.2010.11.021 ↗
6. Effective strain—fatigue life data for variable amplitude fatigue10.1016/S0142-1123(97)00013-3 ↗
7. Fatigue: A complex subject—Some simple approximations10.1007/BF02321056 ↗
8. A study of the effects of cyclic thermal stresses on a ductile met
9. Fundamentals of modern fatigue analysis. Fatigue Fract., ASM
10. A modified universal slopes equation for estimation of fatigue characteristics of metals10.1115/1.3226010 ↗
11. An improved technique for the prediction of axial fatigue life from tensile data10.1016/0142-1123(93)90179-T ↗
12. Seeger · 1990
13. Strain-controlled fatigue properties of steels and some simple approximations10.1016/S0142-1123(00)00026-8 ↗
14. Statistical evaluation of strain-life fatigue crack initiation predictions10.1016/j.ijfatigue.2003.10.003 ↗
15. On the estimation of cyclic material properties-Part 2: Introduction of a new estimation method: Dedicated to Professor Dr.-Ing. Harald Zenner on the occasion of his eightieth birthday. Mater
16. Determining the fatigue parameters in total strain life equation of a material based on monotonic tensile mechanical properties10.1016/j.engfracmech.2019.106866 ↗
17. Physics-guided machine learning frameworks for fatigue life prediction of AM materials10.1016/j.ijfatigue.2023.107658 ↗
18. Hybrid enhanced Monte Carlo simulation coupled with advanced machine learning approach for accurate and efficient structural reliability analysis10.1016/j.cma.2021.114218 ↗
19. EMCS-SVR: Hybrid efficient and accurate enhanced simulation approach coupled with adaptive SVR for structural reliability analysis10.1016/j.cma.2022.115499 ↗
20. A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction10.1016/j.ijfatigue.2022.106996 ↗
21. A novel method of multiaxial fatigue life prediction based on deep learning10.1016/j.ijfatigue.2021.106356 ↗
22. Very high-cycle fatigue life prediction of high-strength steel based on machine learning10.1111/ffe.14213 ↗
23. Symbolic regression in materials science10.1557/mrc.2019.85 ↗
24. Multi-objective symbolic regression for physics-aware dynamic modeling10.1016/j.eswa.2021.115210 ↗
25. Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression10.1186/s10033-023-00876-8 ↗
26. High-cycle fatigue life prediction of L-PBF AlSi10Mg alloys: a domain knowledge-guided symbolic regression approach
27. A novel fatigue and creep-fatigue life prediction model by combining data-driven approach with domain knowledge10.1016/j.ijfatigue.2024.108402 ↗
28. Interpretable machine learning for microstructure-dependent models of fatigue indicator parameters10.1016/j.ijfatigue.2023.108019 ↗
29. Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys10.1016/j.probengmech.2013.08.004 ↗
30. Uncertainty quantification in fatigue crack-growth predictions10.1007/s10704-022-00624-4 ↗
31. Uncertainty theory based reliability modeling for fatigue10.1016/j.engfailanal.2020.104931 ↗
32. Zone-based reliability analysis on fatigue life of GH720Li turbine disk concerning uncertainty quantification10.1016/j.ast.2017.08.011 ↗
33. Fatigue life uncertainty prediction using the Monte Carlo and Latin hypercube sampling techniques under uniaxial and multiaxial cyclic loading10.1016/j.ijfatigue.2022.106867 ↗
34. A comprehensive evaluation of conventional methods for estimation of fatigue parameters of steels from their monotonic properties10.1016/j.ijfatigue.2024.108244 ↗
35. Analysis of strain-life fatigue parameters and behaviour of different groups of metallic materials10.1016/j.ijfatigue.2010.10.005 ↗
36. Low-cycle fatigue parameters and fatigue life estimation of high-strength steels with artificial neural networks10.1111/ffe.13847 ↗
37. Application of artificial neural network for predicting strain-life fatigue properties of steels on the basis of tensile tests10.1016/j.ijfatigue.2004.03.009 ↗
38. Strain-life curves of steels and methods for determining the curve parameters. Part 2. Methods based on the use of artificial neural networks10.1007/s11223-011-9262-4 ↗
39. A computational estimation of cyclic material properties using artificial neural networks10.1016/j.proeng.2011.04.075 ↗
40. Estimation of strain controlled fatigue properties of steels using tensile test data
41. Artificial neural network technique as a method to evaluate the strain-life fatigue properties of steel weldments on the basis of tensile tests
42. Estimation and validation for fatigue properties of steels by symbolic regression10.1016/j.ijfatigue.2024.108416 ↗
43. Opening the black box: interpretable machine learning for geneticists10.1016/j.tig.2020.03.005 ↗
44. A value for n-person games
45. Lundberg SM, Lee S-I. A unified approach to interpreting model predictions. In: Guyon I, Luxburg UV, Bengio S, Wallach H, Fergus R, Vishwanathan S, et al., editors. Adv. Neural Inf. Process. Syst., vol. 30, Curran Associates, Inc.; 2017.
46. Relation between prognostics predictor evaluation metrics and local interpretability SHAP values10.1016/j.artint.2022.103667 ↗
47. Insights into geospatial heterogeneity of landslide susceptibility based on the SHAP-XGBoost model10.1016/j.jenvman.2023.117357 ↗
48. An interpretable prediction model for identifying N7-Methylguanosine sites based on XGBoost and SHAP10.1016/j.omtn.2020.08.022 ↗
49. Feature selection strategies: a comparative analysis of SHAP-value and importance-based methods10.1186/s40537-024-00905-w ↗
50. Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., San Francisco California USA: ACM; 2016, p. 785–94. Doi: 10.1145/2939672.2939785.10.1145/2939672.2939785 ↗
51. The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis10.1016/j.aap.2021.106153 ↗
52. A physics-inspired method for symbolic regression10.1126/sciadv.aay2631 ↗
53. Data-driven discovery of dimensionless numbers and governing laws from scarce measurements10.1038/s41467-022-35084-w ↗
54. Artificial intelligence in physical sciences: symbolic regression trends and perspectives10.1007/s11831-023-09922-z ↗
55. Interpretable scientific discovery with symbolic regression: a review10.1007/s10462-023-10622-0 ↗
56. Cranmer, M. Interpretable machine learning for science with PySR and SymbolicRegression. jl, 2023. arXiv preprint arXiv:2305.01582.
57. Cranmer, M. Discovering symbolic models from deep learning with inductive biases. arXiv preprint arXiv. 2020, 2006.11287.
58. Establish algebraic data-driven constitutive models for elastic solids with a tensorial sparse symbolic regression method and a hybrid feature selection technique10.1016/j.jmps.2021.104742 ↗
59. Quantifying colocalization by correlation: The Pearson correlation coefficient is superior to the Mander’s overlap coefficient10.1002/cyto.a.20896 ↗
60. Extracting spatial effects from machine learning model using local interpretation method: An example of SHAP and XGBoost10.1016/j.compenvurbsys.2022.101845 ↗
61. Probabilistic physics of failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty10.1016/j.ress.2015.10.002 ↗
62. Uncertainty quantification and model validation of fatigue crack growth prediction10.1016/j.engfracmech.2011.02.017 ↗
63. Low cycle fatigue behaviour of a medium carbon microalloyed steel10.1016/S0142-1123(96)00060-6 ↗
64. Boller · 1987
65. Low cycle fatigue behavior of 316LN stainless steel: effects of temperature, strain rate and strain amplitude10.1016/j.ijfatigue.2023.107767 ↗
66. Effects of strain rate on low cycle fatigue behaviors of high-strength structural steel10.1016/S1006-706X(13)60126-0 ↗
67. Temperature and strain-rate effects on low-cycle fatigue behavior of alloy 800H10.1007/BF02648404 ↗
68. Strain-rate effect on high temperature low-cycle fatigue deformation of AISI 304L stainless steel10.1007/BF01154006 ↗
69. Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties10.1108/IJSI-06-2023-0048 ↗
70. Structural dynamic reliability analysis: review and prospects10.1108/IJSI-04-2022-0050 ↗
71. Multiaxial low-cycle fatigue life model for notched specimens considering small sample characteristics10.1108/IJSI-05-2024-0071 ↗