journal article·2024-11

A novel nonlinear fatigue cumulative damage model based on machine learning

Zhiyuan Gao, Xiaomo Jiang, Mingqing Cui

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Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

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Research context

OpenAlex topics: Fatigue and fracture mechanics · Material Properties and Failure Mechanisms · Hydrogen embrittlement and corrosion behaviors in metals

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References (43)

Show source-provided references
1. Notch fatigue behavior of a titanium alloy in the VHCF regime based on a vibration fatigue test
2. High cycle fatigue characterization of a nickel-based superalloy based on a novel temperature-dependent regression method10.1016/j.ijfatigue.2020.105960 ↗
3. Cumulative fatigue damage theories for metals: review and prospects10.1108/IJSI-09-2022-0119 ↗
4. A nonlinear cumulative fatigue damage life prediction model under combined cycle fatigue loading considering load interaction
5. Practical implementation of the double linear damage rule and damage curve approach for treating cumulative fatigue damage10.1007/BF00053519 ↗
6. A nonlinear fatigue damage accumulation model under variable amplitude loading considering the loading sequence effect
7. A new fatigue cumulative damage model based on material parameters and stress interaction
8. An improved Manson-Halford Model for Multi-level nonlinear fatigue life prediction
9. Data-driven evaluation of fatigue performance of additive manufactured parts using miniature specimens10.1016/j.jmst.2018.12.011 ↗
10. A multi-algorithm integration machine learning approach for high cycle fatigue prediction of a titanium alloy in aero-engine
11. Machine learning for molecular and materials science10.1038/s41586-018-0337-2 ↗
12. Validation of linear damage rules using random loading10.1177/1056789515605881 ↗
13. Non-linear incremental fatigue damage calculation for multiaxial non-proportional histories10.1016/j.ijfatigue.2016.12.008 ↗
14. Miner. Cumulative Damage in Fatigue. Journal of Applied Mechanics 1945;12:A159-A64.10.1115/1.4009458 ↗
15. Fatigue life prediction under variable amplitude loading using a microplasticity-based constitutive model10.1016/j.ijfatigue.2020.105477 ↗
16. A one-parameter nonlinear fatigue damage accumulation model10.1016/j.ijfatigue.2017.01.039 ↗
17. An improved fatigue accumulation damage model based on load interaction and strength degradation10.1016/j.ijfatigue.2021.106636 ↗
18. On the development of error-trained BP-ANN technique with CDM model for the HCF life prediction of aluminum alloy
19. Crack growth rate prediction based on damage accumulation functions for creep-fatigue interaction
20. A nonlinear fatigue damage accumulation model considering strength degradation and its applications to fatigue reliability analysis10.1177/1056789514544228 ↗
21. Reliability analysis based on a nonlinear fatigue damage accumulation model
22. Huiying G, Hongzhong H, Zhu Shunpeng, Yanfeng L, Rong Y. A Modified Nonlinear Damage Accumulation Model for Fatigue Life Prediction Considering Load Interaction Effects. The Scientific World Journal 2014;2014:164378.10.1155/2014/164378 ↗
23. A fatigue damage accumulation model for reliability analysis of engine components under combined cycle loadings10.1111/ffe.13246 ↗
24. Variable amplitude fatigue life prediction of rock samples under completely reversed loading
25. Open-access experiment dataset for fatigue damage accumulation and life prediction models10.3390/met13030621 ↗
26. Fatigue life prediction under variable loading based on a new non-linear continuum damage mechanics model10.1016/j.ijfatigue.2005.05.001 ↗
27. Study on nonliner continuous damage cumulative model for uniaxial fatigue
28. Experimental study on fatigue damage under two level loading
29. Fatigue damage behavior of a structural component made of P355NL1 steel under block loading
30. A cumulative damage rule based on the knee point of the S-N curve10.1115/1.3443383 ↗
31. New continuous fatigue damage model based on whole damage field measurement
32. SN curve movement-based life analytical model
33. A phenomenological fatigue damage accumulation rule based on hardness increasing, for the 2024–T42 aluminum10.1016/S0141-0296(02)00055-X ↗
34. Loh WY. Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2011;1:14-23.10.1002/widm.8 ↗
35. Fatigue life prediction for vibration isolation rubber based on parameter-optimized support vector machine model
36. Chen T, Guestrin C. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining2016. p. 785-94.10.1145/2939672.2939785 ↗
37. Machine learning approaches to rock fracture mechanics problems: Mode-I fracture toughness determination
38. Edward RC, I. WCK. Gaussian Processes for Machine Learning: The MIT Press; 2006.
39. Erickson N, Mueller J, Shirkov A, Zhang H, Larroy P, Li M, et al. Autogluon-tabular: Robust and accurate automl for structured data. arXiv preprint 2020;arXiv:2003.06505.
40. Gaussian process regression based remaining fatigue life prediction for metallic materials under two-step loading10.1016/j.ijfatigue.2022.106730 ↗
41. Estimation of remaining fatigue life under two-step loading based on kernel-extreme learning machine10.1016/j.ijfatigue.2021.106190 ↗
42. The many shapley values for model explanation
43. High cycle fatigue life prediction of titanium alloys based on a novel deep learning approach10.1016/j.ijfatigue.2024.108206 ↗