journal article·2023-05

A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace

W.Q. Hao, L. Tan, X.G. Yang, D.Q. Shi, M.L. Wang, G.L. Miao, Y.S. Fan

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 · Hydrogen embrittlement and corrosion behaviors in metals

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

References (57)

Show source-provided references
1. Theory of notch stresses: principles for exact calculation of strength with reference to structural form and material
2. On the fatigue notch factor10.1016/0142-1123(95)93538-D ↗
3. Fatigue process zone at notches10.1016/S0142-1123(01)00004-4 ↗
4. The theory of critical distances10.1016/j.engfracmech.2007.04.007 ↗
5. Neuber · Theorie der technischen Formzahl VDI-Forschungsh · 1936 · 7 · 271
6. Peterson · Notch sensitivity Metal Fatigue · 1959 · 293
7. Geometrical effects in fatigue: a unifying theoretical model10.1016/S0142-1123(99)00007-9 ↗
8. The fatigue behaviour of three-dimensional stress concentrations10.1016/j.ijfatigue.2004.07.006 ↗
9. A unifying approach to estimate the high-cycle fatigue strength of notched components subjected to both uniaxial and multiaxial cyclic loadings10.1111/j.1460-2695.2004.00759.x ↗
10. On the application of multiaxial high-cycle fatigue criteria using the theory of critical distances10.1016/j.engfracmech.2008.10.014 ↗
11. A combined critical plane and critical distance approach for predicting fatigue crack initiation in notched single-crystal superalloy components10.1016/j.ijfatigue.2011.05.009 ↗
12. High temperature LCF life prediction of notched DS Ni-based superalloy using critical distance concept10.1016/j.ijfatigue.2011.05.018 ↗
13. Experimental determination and sensitivity analysis of the fatigue critical distance obtained with rounded V-notched specimens10.1016/j.ijfatigue.2018.03.037 ↗
14. Stress field intensity approach for predicting fatigue life10.1016/0142-1123(93)90182-P ↗
15. Local stress-strain field intensity approach to fatigue life prediction under random cyclic loading10.1016/S0142-1123(01)00051-2 ↗
16. A review of the volume-based strain energy density approach applied to V-notches and welded structures10.1016/j.tafmec.2009.10.001 ↗
17. State-of-the-art review on the local strain energy density concept and its relation to the J-integral and peak stress method10.1111/ffe.12231 ↗
18. Multiaxial fatigue of V-notched steel specimens: A non-conventional application of the local energy method10.1111/j.1460-2695.2011.01585.x ↗
19. Data-driven fatigue life prediction in additive manufactured titanium alloy: A damage mechanics based machine learning framework
20. A machine learning approach to the prediction of fretting fatigue life
21. Fatigue life prediction considering mean stress effect based on random forests and kernel extreme learning machine
22. Unified fatigue life modelling and uncertainty estimation of Ni-based superalloy family with a supervised machine learning approach
23. Application of the Gaussian process for fatigue life prediction under multiaxial loading
24. Gaussian Process for Machine Learning-Based Fatigue Life Prediction Model under Multiaxial Stress-Strain Conditions10.3390/ma15217797 ↗
25. Multiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and Gaussian process regression
26. Practical options for selecting data-driven or physics-based prognostics algorithms with reviews10.1016/j.ress.2014.09.014 ↗
27. Physics-informed neural networks for missing physics estimation in cumulative damage models: A case study in corrosion fatigue
28. A physics-informed neural network approach to fatigue life prediction using small quantity of samples
29. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network
30. Yan · 2002
31. Probabilistic fatigue modelling of metallic materials under notch and size effect using the weakest link theory10.1016/j.ijfatigue.2022.106788 ↗
32. Fatigue life prediction of nickel-based GH4169 alloy on the basis of a multi-scale crack propagation approach10.1016/j.engfracmech.2018.05.023 ↗
33. Assessment of notch fatigue and size effect using stress field intensity approach10.1016/j.ijfatigue.2021.106279 ↗
34. Comparison of the fatigue and fracture of Ti-6Al-2Zr-1Mo-1V with lamellar and bimodal microstructures10.1016/j.msea.2007.02.064 ↗
35. Fatigue life prediction techniques for notch geometries in titanium alloys10.1016/S0142-1123(99)00071-7 ↗
36. Notch fatigue behavior of Ti-6Al-4V alloy in transition region between low and high cycle fatigue10.1016/j.ijfatigue.2016.10.024 ↗
37. Fatigue life prediction of small notched Ti-6Al-4V specimens using critical distance10.1016/j.engfracmech.2010.04.001 ↗
38. Fatigue strength of severely notched specimens made of Ti-6Al-4V under multiaxial loading10.1111/ffe.12272 ↗
39. Multiaxial variable amplitude fatigue life analysis including notch effects10.1016/j.ijfatigue.2015.12.011 ↗
40. Multiaxial variable amplitude fatigue life analysis using the critical plane approach, Part II: Notched specimen experiments and life estimations10.1016/j.ijfatigue.2017.09.009 ↗
41. Navathe · 1997
42. Fatigue analysis and the local stress-strain approach in complex vehicular structures10.1016/S0142-1123(97)00045-5 ↗
43. Comparison of different one-parameter damage laws and local stress-strain approaches in multiaxial fatigue life assessment of notched components10.1016/j.ijfatigue.2021.106405 ↗
44. Stress-strain function for the fatigue of metals
45. Cortes · Support-Vector Networks Machine Leaming · 1995 · 20 · 273
46. Fundamentals of optimization: Methods, minimum principles, and applications for making things better
47. Random Forests10.1023/A:1010933404324 ↗
48. Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2016;13-17-August-2016:785–94. https://doi.org/10.1145/2939672.2939785.10.1145/2939672.2939785 ↗
49. Scikit-learn: Machine learning in Python
50. Algorithms for Hyper-Parameter Optimization
51. Fatigue Strength of TC18 Titanium Alloy Forgings with Different Stress Concentrations and Stress Ratios
52. Factorial sampling plans for preliminary computational experiments10.1080/00401706.1991.10484804 ↗
53. Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates10.1016/S0378-4754(00)00270-6 ↗
54. A quantitative model-independent method for global sensitivity analysis of model output10.1080/00401706.1999.10485594 ↗
55. SALib: An open-source Python library for Sensitivity Analysis10.21105/joss.00097 ↗
56. Notch effect on multiaxial low cycle fatigue10.1016/j.ijfatigue.2011.01.011 ↗
57. The effect of notch geometry on critical distance high cycle fatigue predictions10.1016/j.ijfatigue.2005.06.017 ↗