On the integration of domain knowledge and branching neural network for fatigue life prediction with small samples
Bibliographic record
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: Smart Materials for Construction · Fatigue and fracture mechanics · Infrastructure Maintenance and Monitoring
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (85)
Show source-provided references
1. Suresh · 1991
2. Generalized probabilistic model allowing for various fatigue damage variables10.1016/j.ijfatigue.2017.03.031 ↗
3. Nonlinear fatigue damage accumulation and life prediction of metals: A comparative study10.1111/ffe.12937 ↗
4. Die · Z Ver Dtsch Ing · 1924 · 68 · 339
5. Miner M.A. Cumulative damage in fatigue. J Appl Mech, 1945, 67: A159‐A164.10.1115/1.4009458 ↗
6. Versuche zur Ermittlung der auf die Eisenbahnwagenachsen einwirkenden Kraft und die Widerstandsfahigkeit der Wagen Achsen
7. Morrow J. Cyclic plastic strain energy and the fatigue of metals. In: Internal friction, damping and cyclic plasticity, ASTM STP Vol 378, 1965: 45-84.10.1520/STP43764S ↗
8. Multiaxial fatigue damage models10.1115/1.3225980 ↗
9. A critical review on multiaxial fatigue assessments of metals10.1016/0142-1123(96)00002-3 ↗
10. Cumulative fatigue damage and life prediction theories: a survey of the state of the art for homogeneous materials10.1016/S0142-1123(97)00081-9 ↗
11. A Review of Critical Plane Orientations in Multiaxial Fatigue Failure Criteria of Metallic Materials10.1007/s10704-005-1088-2 ↗
12. Mean stress effects in strain-life fatigue10.1111/j.1460-2695.2009.01404.x ↗
13. Nonlinear fatigue damage accumulation: Isodamage curve-based model and life prediction aspects10.1016/j.ijfatigue.2019.105185 ↗
14. A mean stress correction model for tensile and compressive mean stress fatigue loadings10.1111/ffe.12553 ↗
15. Statistical evaluation of strain-life fatigue crack initiation predictions10.1016/j.ijfatigue.2003.10.003 ↗
16. A modified nonlinear fatigue damage accumulation model10.1177/1056789514524075 ↗
17. A one-parameter nonlinear fatigue damage accumulation model10.1016/j.ijfatigue.2017.01.039 ↗
18. Mean stress effect correction in strain energy-based fatigue life prediction of metals10.1177/1056789516651920 ↗
19. Multiaxial fatigue life prediction based on a simplified energy-based model10.1016/j.ijfatigue.2020.106036 ↗
20. Creep-fatigue life prediction in nickel-based superalloy GH4169 based on microstructural damage quantification with the help of electron backscatter diffraction
21. Intelligent Fault Diagnosis of Rolling Bearing Using Deep Wavelet Auto-encoder with Extreme Learning Machine10.1016/j.knosys.2017.10.024 ↗
22. Fatigue damage effect approach by artificial neural network10.1016/j.ijfatigue.2019.02.043 ↗
23. Machine Learning of Phases and Mechanical Properties in Complex Concentrated Alloys10.1016/j.jmst.2021.01.054 ↗
24. A machine-learning fatigue life prediction approach of additively manufactured metals10.1016/j.engfracmech.2020.107508 ↗
25. Neuro-fuzzy modeling of fatigue life prediction of unidirectional glass fiber/epoxy composite laminates10.1177/0021998302036006176 ↗
26. Fatigue life prediction of bending polymer films using random forest10.1016/j.ijfatigue.2022.107230 ↗
27. Multiaxial fatigue life prediction method based on the back-propagation neural network10.1016/j.ijfatigue.2022.107274 ↗
28. Data-driven fatigue life prediction in additive manufactured titanium alloy: A damage mechanics based machine learning framework10.1016/j.engfracmech.2021.107850 ↗
29. A novel method of multiaxial fatigue life prediction based on deep learning10.1016/j.ijfatigue.2021.106356 ↗
30. A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions10.1016/j.ijfatigue.2021.106236 ↗
31. Machine learning-based genetic feature identification and fatigue life prediction
32. Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression10.1016/j.ijfatigue.2021.106415 ↗
33. A Probabilistic Fatigue Life Prediction for Adhesively Bonded Joints via ANNs-based Hybrid Model10.1016/j.ijfatigue.2021.106352 ↗
34. Statistical learning of small data with domain knowledge –-sample size- and pre-notch length- dependent strength of concrete10.1016/j.engfracmech.2021.108160 ↗
35. On the use of data-driven machine learning for remaining life estimation of metallic materials based on Ye-Wang damage theory
36. Wang Y, Yao Q, James K, Lionel M N. Generalizing from a Few Examples: A Survey on Few-Shot Learning. arXiv preprint arXiv:1904.05046, 2019.
37. Determination of the fatigue P-S-N curves-A critical review and improved backward statistical inference method10.1016/j.ijfatigue.2020.105789 ↗
38. A new cyclical generative adversarial network based data augmentation method for multiaxial fatigue life prediction10.1016/j.ijfatigue.2022.106996 ↗
39. Application of tabular data synthesis using generative adversarial networks on machine learning-based multiaxial fatigue life prediction10.1016/j.ijpvp.2022.104779 ↗
40. Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data10.1109/TKDE.2017.2720168 ↗
41. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures10.1016/j.engfracmech.2021.108130 ↗
42. Process-Guided Deep Learning Predictions of Lake Water Temperature10.1029/2019WR024922 ↗
43. Thermodynamics-based Artificial Neural Networks for constitutive modeling10.1016/j.jmps.2020.104277 ↗
44. Physics-Informed Neural Networks for Missing Physics Estimation in Cumulative Damage Models: A Case Study in Corrosion Fatigue10.1115/1.4047173 ↗
45. A physics-informed neural network approach to fatigue life prediction using small quantity of samples10.1016/j.ijfatigue.2022.107270 ↗
46. Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts10.1016/j.ijfatigue.2023.107500 ↗
47. A critical plane approach to multiaxialfatigue damage includingout-of-phase loading10.1111/j.1460-2695.1988.tb01169.x ↗
48. Review of contemporary approaches to fatigue damage analysis
49. An introduction to neural computing10.1016/0893-6080(88)90020-2 ↗
50. Multilayer feedforward networks are universal approximators10.1016/0893-6080(89)90020-8 ↗
51. Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks10.1016/0893-6080(90)90005-6 ↗
52. Hardware implementation of subtractive clustering for radionuclide identifification10.1016/j.vlsi.2012.10.005 ↗
53. Adaptive neuro-fuzzy inference system in modelling fatigue life of multidirectional composite laminates10.1016/j.commatsci.2008.02.028 ↗
54. Fuzzy Model Identification Based on Cluster Estimation
55. Fatigue life prediction of aluminum alloy via knowledge-based machine learning10.1016/j.ijfatigue.2021.106716 ↗
56. A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace10.1016/j.ijfatigue.2023.107536 ↗
57. On the development of error-trained BP-ANN technique with CDM model for the HCF life prediction of aluminum alloy10.1016/j.ijfatigue.2022.106836 ↗
58. Fatigue life prediction in presence of mean stresses using domain knowledge-integrated ensemble of extreme learning machines10.1111/ffe.13792 ↗
59. Estimation of elastic-plastic notch strains and stresses using artificial neural networks10.1111/ffe.13540 ↗
60. Predicting fatigue lifetimes of a reinforced membrane in polymer electrolyte membrane fuel cell using plastic energy10.1016/j.jpowsour.2022.231597 ↗
61. Sebastian R. An Overview of Multi-Task Learning in Deep Neural Networks. arXiv preprint arXiv:1706.05098, 2017.
62. Low-cycle multiaxial fatigue behaviour and fatigue life prediction for CuZn37 brass using the stress-strain models10.1016/j.ijfatigue.2017.04.011 ↗
63. An experimental evaluation of three critical plane multiaxial fatigue criteria10.1016/j.ijfatigue.2006.10.028 ↗
64. Multiaxial Fatigue of 16MnR Steel
65. Multiaxial fatigue of titanium including step loading and load path alteration and sequence effects10.1016/j.ijfatigue.2010.05.006 ↗
66. Multiaxial low-cycle fatigue life evaluation under different non-proportional loading paths10.1111/ffe.12752 ↗
67. 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 ↗
68. Multiaxial fatigue life prediction for titanium alloy TC4 under proportional and nonproportional loading10.1016/j.ijfatigue.2013.08.028 ↗
69. Evaluation of multiaxial fatigue life prediction criteria for Ni-based superalloy GH4169
70. Fatigue of 7075–T651 aluminum alloy10.1016/j.ijfatigue.2007.07.005 ↗
71. Load path sensitivity and fatigue life estimation of 30CrNiMo8HH10.1016/j.ijfatigue.2011.10.009 ↗
72. Testing and assessment of fatigue life prediction models for Indian PHWRs piping material under multi-axial load cycling10.1016/j.ijfatigue.2015.12.002 ↗
73. Socie DF, Kurath P, Koch J. A multiaxial fatigue damage parameter. In: Biaxial and multiaxial fatigue, EGF. Vol.3, 1989: 535-550.
74. Multiaxial fatigue of 2024–T4 aluminum alloy10.3901/CJME.2011.02.195 ↗
75. Variable amplitude cyclic deformation and fatigue behaviour of stainless steel 304L including step, periodic, and random loadings
76. Fatigue life estimation for 2017A–T4 and 6082–T6 aluminium alloys subjected to bending-torsion with mean stress10.1016/j.ijfatigue.2015.05.005 ↗
77. Fatigue of beta titanium alloy at 20, 482 and 648 °C10.1111/j.1460-2695.2004.00805.x ↗
78. Low-cycle fatigue of austempered ductile irons at various strain ratios10.1016/S0142-1123(98)00053-X ↗
79. Fatigue damage evaluation of a high pressure tube steel using cyclic strain energy density10.1016/S0308-0161(02)00135-7 ↗
80. Effects of mean stress on fatigue behaviour of a hardened carbon steel10.1016/0142-1123(91)90248-W ↗
81. Mean stress models for low-cycle fatigue of a nickel-base superalloy10.1016/0142-1123(94)90458-8 ↗
82. Mean stress effects on low cycle fatigue for a high strength steel10.1111/j.1460-2695.1991.tb00672.x ↗
83. Sequential law in multiaxial fatigue, a new damage indicator10.1016/j.ijfatigue.2004.08.005 ↗
84. Integration of symbolic regression and domain knowledge for interpretable modeling of remaining fatigue life under multistep loading
85. How transferable are features in deep neural networks?