Data-Enhanced Low-Cycle Fatigue Life Prediction Model Based on Nickel-Based Superalloys
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: Fatigue and fracture mechanics · High Temperature Alloys and Creep · Metallurgy and Material Forming
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Thellaputta GR, Chandra PS, Rao CSP. Machinability of nickel based superalloys: a review. Mater Today Proce. 2017;4:3712–21.10.1016/j.matpr.2017.02.266 ↗
2. Coffin LF Jr. A study of the effects of cyclic thermal stresses on a ductile metal. Trans Am Soc Mech Eng. 1954;76(6):931–49.10.1115/1.4015020 ↗
3. Manson SS. Behavior of materials under conditions of thermal stress. National Advisory Committee for Aeronautics; 1953.
4. Wang RZ, Zhang XC, Gong JG, et al. Creep-fatigue life prediction and interaction diagram in nickel-based GH4169 superalloy at 650 C based on cycle-by-cycle concept. Int J Fatigue. 2017;97:114–23.10.1016/j.ijfatigue.2016.11.021 ↗
5. Voet V, Van Loock F, De Fruytier C, et al. Machine learning aided modelling of thermomechanical fatigue of solder joints in electronic component assemblies. Int J Fatigue. 2023;167:107298.10.1016/j.ijfatigue.2022.107298 ↗
6. Chandran KSR. A physical model and constitutive equations for complete characterization of SN fatigue behavior of metals. Acta Mater. 2016;121:85–103.10.1016/j.actamat.2016.09.001 ↗
7. Wang Y, Zhu Z, Sha A, et al. Low cycle fatigue life prediction of titanium alloy using genetic algorithm-optimized BP artificial neural network. Int J Fatigue. 2023;172:107609.10.1016/j.ijfatigue.2023.107609 ↗
8. Zhan Z, Hu W, Meng Q. Data-driven fatigue life prediction in additive manufactured titanium alloy: a damage mechanics based machine learning framework. Eng Fract Mech. 2021;252:107850.10.1016/j.engfracmech.2021.107850 ↗
9. Zhan Z, Li H. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L. Int J Fatigue. 2021;142:105941.10.1016/j.ijfatigue.2020.105941 ↗
10. Yan F, Song K, Liu Y, et al. Predictions and mechanism analyses of the fatigue strength of steel based on machine learning. J Mater Sci. 2020;55:15334–49.10.1007/s10853-020-05091-7 ↗
11. Venkatesh V, Rack HJ. A neural network approach to elevated temperature creep–fatigue life prediction. Int J Fatigue. 1999;21(3):225–34.10.1016/S0142-1123(98)00071-1 ↗
12. He GY, Zhao YX, Yan CL. Application of tabular data synthesis using generative adversarial networks on machine learning-based multiaxial fatigue life prediction. Int J Press Vessels Pip. 2022;199:104779.10.1016/j.ijpvp.2022.104779 ↗
13. Kingma DP, Welling M. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114; 2013.
14. Wu J, Plataniotis K, Liu L, et al. Interpretation for variational autoencoder used to generate financial synthetic tabular data. Algorithms. 2023;16(2):121.10.3390/a16020121 ↗
15. Ai Q, Wang P, He L, et al. Generative oversampling for imbalanced data via majority-guided VAE. International Conference on Artificial Intelligence and Statistics, PMLR, 2023: 3315–30.
16. Islam Z, Abdel-Aty M, Cai Q, et al. Crash data augmentation using variational autoencoder. Accid Anal Prev. 2021;151:105950.10.1016/j.aap.2020.105950 ↗
17. Qian H, Huang Z, Xu Y, et al. Very high cycle fatigue life prediction of Ti60 alloy based on machine learning with data enhancement. Eng Fract Mech. 2023;289:109431.10.1016/j.engfracmech.2023.109431 ↗
18. Cutler A, Cutler DR, Stevens JR. Random forests. Ensemble machine learning: Methods and applications. 2012: 157–75.10.1007/978-1-4419-9326-7_5 ↗
19. Agatonovic-Kustrin S, Beresford R. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research. J Pharm Biomed Anal. 2000;22(5):717–27.10.1016/S0731-7085(99)00272-1 ↗
20. Cortes C, Vapnik V. Support-vector networks. Machine learning. 1995; 20: 273-9710.1023/A:1022627411411 ↗
21. Zhao E, Li Y, Zhang J, Li Z. Interval prediction model of deformation behavior for dam safety during long-term operation using bootstrap-GBDT. Struct Control Health Monit. 2023;2023:1–14. https://doi.org/10.1155/2023/6929861.10.1155/2023/6929861 ↗
22. Liang W, Luo S, Zhao G, et al. Predicting hard rock pillar stability using GBDT, XGBoost, and LightGBM algorithms. Mathematics. 2020;8(5):765.10.3390/math8050765 ↗
23. Zhang Y, Zhao Z, Zheng J. CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China. J Hydrol. 2020;588:125087.10.1016/j.jhydrol.2020.125087 ↗
24. Wang Z, Ogawa T, Adachi Y. Influence of algorithm parameters of bayesian optimization, genetic algorithm, and particle swarm optimization on their optimization performance. Adv Theory Simul. 2019;2(10):1900110.10.1002/adts.201900110 ↗
25. Xu J, Li H, Hou S. Autoencoder-guided GAN for minority-class cloth-changing gait data generation. Digital Signal Process. 2022;128:103608.10.1016/j.dsp.2022.103608 ↗
26. Zhou R, Xing Z, Wang H, et al. Prediction of contact fatigue life of AT40 ceramic coating based on neural network. Anti-Corros Meth Mater. 2020;67(1):83–100.10.1108/ACMM-10-2019-2190 ↗
27. Xu L, Zhang R, Hao M, et al. A data-driven low-cycle fatigue life prediction model for nickel-based superalloys. Comput Mater Sci. 2023;229:112434.10.1016/j.commatsci.2023.112434 ↗
28. Li P, Li QQ, Jin T, et al. Comparison of low-cycle fatigue behaviors between two nickel-based single-crystal superalloys. Int J Fatigue. 2014;63:137–44.10.1016/j.ijfatigue.2014.01.018 ↗
29. Zhang P, Zhu Q, Hu C, et al. Cyclic deformation behavior of a nickel-base superalloy under fatigue loading. Mater Des. 2015;69:12–21.10.1016/j.matdes.2014.12.047 ↗
30. Wang K, Jing H, Xu L, et al. Cyclic response and dislocation evolution of a nickel-based superalloy under low cycle fatigue deformation. Mater Sci Eng, A. 2021;814:141225.10.1016/j.msea.2021.141225 ↗
31. Xu J, Huang Z, Jiang L. Effect of heat treatment on low cycle fatigue of IN718 superalloy at the elevated temperatures. Mater Sci Eng, A. 2017;690:137–45.10.1016/j.msea.2017.02.104 ↗
32. Zhang P, Zhu Q, Chen G, et al. Effect of heat treatment process on microstructure and fatigue behavior of a nickel-base superalloy. Materials. 2015;8(9):6179–94.10.3390/ma8095299 ↗
33. Mahobia GS, Paulose N, Mannan SL, et al. Effect of hot corrosion on low cycle fatigue behavior of superalloy IN718. Int J Fatigue. 2014;59:272–81.10.1016/j.ijfatigue.2013.08.009 ↗
34. Yu J, Sun X, Jin T, et al. High temperature creep and low cycle fatigue of a nickel-base superalloy. Mater Sci Eng A. 2010;527(9):2379–89.10.1016/j.msea.2010.01.001 ↗
35. Yao LL, Zhang XC, Liu F, Tu ST, Ma C. High-temperature low-cycle fatigue properties of GH4169 superalloy. Mater Mech Eng. 2016;40(4):25–9.
36. Sun J, Yuan H. Life assessment of multiaxial thermomechanical fatigue of a nickel-based superalloy Inconel 718. Int J Fatigue. 2019;120:228–40.10.1016/j.ijfatigue.2018.11.018 ↗
37. Chen G, Zhang Y, Xu DK, et al. Low cycle fatigue and creep-fatigue interaction behavior of nickel-base superalloy GH4169 at elevated temperature of 650°C. Mater Sci Eng A. 2016;655:175–82.10.1016/j.msea.2015.12.096 ↗
38. Xu J, Huang Z, Jiang L. Low cycle fatigue behavior of a high Co polycrystalline nickel-base superalloy at 750 °C. J Alloy Compd. 2018;749:1106–14.10.1016/j.jallcom.2018.03.364 ↗
39. Li S, Ping L. Low-cycle fatigue behavior of a nickel base single crystal superalloy at high temperature. Rare Metal Mater Eng. 2015;44(2):288–92.10.1016/S1875-5372(15)30021-7 ↗
40. Qu H, Wang LB, Wang DH, et al. Low-cycle fatigue properties and life prediction of aging strengthened high-nickel Inconel-718 alloy. Heat Treat Met. 2021;46(7):6.
41. Ganji DK, Rajyalakshmi G. Influence of alloying compositions on the properties of nickel-based superalloys:a review. Singapore: Springer; 2020.10.1007/978-981-15-1071-7_44 ↗
42. Salem NM, Deifalla A. Evaluation of the strength of slab-column connections with FRPs using machine learning algorithms. Polymers. 2022;14(8):1517.10.3390/polym14081517 ↗