journal article·2025-08

A novel neural network model considering cyclic loading condition for low-cycle fatigue life prediction

Hongguang Zhou, Ziming Wang, Yunpeng Zhao, Congjie Kang, Xiaohui Yu

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OpenAlex topics: Fatigue and fracture mechanics · Non-Destructive Testing Techniques · Mechanical Failure Analysis and Simulation

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References (48)

Show source-provided references
1. The influence of energy density on the low cycle fatigue behaviour of laser powder bed fused stainless steel 316L[J]10.1016/j.ijfatigue.2023.108123 ↗
2. Effect of helium bubbles on irradiation hardening of additive manufacturing 316L stainless steel under high temperature He ions irradiation[J]10.1016/j.jnucmat.2021.152948 ↗
3. Cryogenic mechanical properties of 316L stainless steel fabricated by selective laser melting[J]10.1016/j.msea.2021.141317 ↗
4. A comparative assessment of fatigue deformation behavior of 316 LN SS at ambient and high temperature[J]10.1016/j.msea.2017.04.102 ↗
5. Xie X, Jiang W, Chen J, et al. Cyclic hardening/softening behavior of 316L stainless steel at elevated temperature including strain-rate and strain-range dependence: Experimental and damage-coupled constitutive modeling[J]. International Journal of Plasticity, 2019, 114: 196-214.10.1016/j.ijplas.2018.11.001 ↗
6. High temperature time-dependent low cycle fatigue behaviour of a type 316L (N) stainless steel[J]10.1016/S0142-1123(98)00052-8 ↗
7. Temperature effect on the low-cycle fatigue behavior of type 316L stainless steel: Cyclic non-stabilization and an invariable fatigue parameter[J]10.1016/j.msea.2006.12.035 ↗
8. The tensile and low-cycle fatigue behavior of cold worked 316L stainless steel: influence of dynamic strain aging[J]10.1016/j.ijfatigue.2003.12.002 ↗
9. Mean stress effect in stress-life for hard steels[J]
10. Equivalent energy-based critical plane fatigue damage parameter for multiaxial LCF under variable amplitude loading[J]10.1016/j.ijfatigue.2019.105350 ↗
11. A study of the effects of cyclic thermal stresses on a ductile metal[J]10.1115/1.4015020 ↗
12. Low-cycle fatigue life prediction of austenitic stainless steel alloys: A data-driven approach with identification of key features[J]
13. Manson S S. Fatigue-a complex subject-some simple approximations[R]. 1965.10.1007/BF02321056 ↗
14. Effect of strain rate on low cycle fatigue of 316LN stainless steel with varying nitrogen content: Part-II fatigue life and fracture[J]10.1016/j.ijfatigue.2015.07.034 ↗
15. A machine-learning fatigue life prediction approach of additively manufactured metals[J]10.1016/j.engfracmech.2020.107508 ↗
16. A novel deep learning approach of multiaxial fatigue life-prediction with a self-attention mechanism characterizing the effects of loading history and varying temperature[J]10.1016/j.ijfatigue.2022.106851 ↗
17. Deep learning regression-based stratified probabilistic combined cycle fatigue damage evaluation for turbine bladed disks[J]10.1016/j.ijfatigue.2022.106812 ↗
18. Deep learning-based modeling method for probabilistic LCF life prediction of turbine blisk[J]10.1016/j.jppr.2023.08.005 ↗
19. Prediction of 316 stainless steel low-cycle fatigue life based on machine learning[J]10.1038/s41598-023-33354-1 ↗
20. Machine learning framework for predicting the low cycle fatigue life of lead-free solders[J]10.1016/j.engfailanal.2023.107228 ↗
21. A data-driven low-cycle fatigue life prediction model for nickel-based superalloys[J]10.1016/j.commatsci.2023.112434 ↗
22. A deep learning approach for low-cycle fatigue life prediction under thermal–mechanical loading based on a novel neural network model[J]10.1016/j.engfracmech.2024.110239 ↗
23. Heterogeneous feature models and feature selection applied to bearing fault diagnosis[J]10.1109/TIE.2014.2327589 ↗
24. Ensemble feature selection: Homogeneous and heterogeneous approaches[J]10.1016/j.knosys.2016.11.017 ↗
25. Effects of strain rate on low cycle fatigue behaviors of high-strength structural steel[J]10.1016/S1006-706X(13)60126-0 ↗
26. Effect of the strain rate on the low cycle fatigue behavior of a 10Cr-2W-Mo-3Co-NbV steel at 650 C[J]10.1016/j.ijfatigue.2017.03.025 ↗
27. A neural network model to predict low cycle fatigue life of nitrogen-alloyed 316L stainless steel[J]10.1016/j.msea.2007.04.018 ↗
28. Low cycle fatigue and creep–fatigue interaction behavior of 316L (N) stainless steel and life prediction by artificial neural network approach[J]10.1016/S0142-1123(03)00064-1 ↗
29. Effect of strain wave shape on high temperature fatigue life of a type 316 steel and application of the strain range partitioning method[J]10.1007/BF02655121 ↗
30. The low-cycle fatigue, deformation and final fracture behaviour of an austenitic stainless steel[J]10.1016/j.msea.2005.09.081 ↗
31. Comparative evaluation of strain controlled low cycle fatigue behaviour of solution annealed and prior cold worked 316L (N) stainless steel[J]10.1016/j.ijfatigue.2004.05.003 ↗
32. Fatigue and cycle plastic behavior of 316L austenitic stainless steel under asymmetric load[J]
33. On the factors influencing the elastoplastic cyclic response and low cycle fatigue failure of AISI 316L steel produced by laser-powder bed fusion[J]10.1016/j.ijfatigue.2022.107224 ↗
34. A physically consistent framework for fatigue life prediction using probabilistic physics-informed neural network[J]10.1016/j.ijfatigue.2022.107234 ↗
35. Statistical distribution functions and fatigue of structures[J]10.1016/j.ijfatigue.2005.03.001 ↗
36. Zheng · 2018
37. A survey on feature selection methods[J]10.1016/j.compeleceng.2013.11.024 ↗
38. classification and regression trees[M]//The top ten algorithms in data mining
39. Principal component analysis[J]10.1002/wics.101 ↗
40. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach[J]10.1016/j.ijfatigue.2022.107483 ↗
41. Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation[J]10.1016/j.commatsci.2019.109203 ↗
42. Machine-learning model for prediction of martensitic transformation temperature in NiMnSn-based ferromagnetic shape memory alloys[J]10.1016/j.commatsci.2022.111811 ↗
43. Multiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and Gaussian process regression[J]10.1016/j.ijfatigue.2022.107361 ↗
44. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures[J]10.1016/j.engfracmech.2021.108130 ↗
45. Fatigue failure mechanism and life prediction of a cast duplex stainless steel after thermal aging[J]10.1016/j.ijfatigue.2021.106161 ↗
46. The Coffin-Manson law as a consequence of the statistical nature of the LCF surface damage[J]10.1016/0956-7151(92)90146-6 ↗
47. The effect of grain size and anomalous shape on low cycle fatigue of nickel-based superalloy at elevated temperature[J]10.1016/j.ijfatigue.2024.108526 ↗
48. Mean stress effects on low cycle fatigue for a high strength steel[J]10.1111/j.1460-2695.1991.tb00672.x ↗