Prediction of corrosion fatigue crack growth rate in aluminum alloys based on incremental learning strategy
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OpenAlex topics: Non-Destructive Testing Techniques · Fatigue and fracture mechanics · Welding Techniques and Residual Stresses
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References (54)
Show source-provided references
1. Development and applications of aluminum alloys for aerospace industry10.1016/j.jmrt.2023.09.274 ↗
2. Aluminium alloys as structural material: A review of research10.1016/j.engstruct.2020.111372 ↗
3. A review: drilling performance and hole quality of aluminium alloys for aerospace applications10.1016/j.jmrt.2020.09.003 ↗
4. Recent advances in the development of aerospace materials10.1016/j.paerosci.2018.01.001 ↗
5. T. Dursun, C. Soutis, Recent developments in advanced aircraft aluminium alloys, Materials & Design (1980-2015), 56 (2014) 862-871. Doi: 10.1016/j.matdes.2013.12.002.10.1016/j.matdes.2013.12.002 ↗
6. Recent development in aluminium alloys for aerospace applications10.1016/S0921-5093(99)00674-7 ↗
7. A machine-learning fatigue life prediction approach of additively manufactured metals10.1016/j.engfracmech.2020.107508 ↗
8. Multi-source data driven fatigue failure analysis and life prediction of pre-corroded aluminum-lithium alloy 2050–T810.1016/j.engfracmech.2023.109626 ↗
9. Assessment of fatigue life of a pre-corroded aircraft wing under drag alternating load10.1016/j.engfailanal.2020.104671 ↗
10. T.Q. Chen, C. Guestrin, M. Assoc Comp, XGBoost: A Scalable Tree Boosting System, in: 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), San Francisco, CA, 2016, pp. 785-794. Doi: 10.1145/2939672.2939785.10.1145/2939672.2939785 ↗
11. Data-Driven Materials Modeling with XGBoost Algorithm and Statistical Inference Analysis for Prediction of Fatigue Strength of Steels10.1007/s12541-019-00048-6 ↗
12. Embracing Change: Continual Learning in Deep Neural Networks10.1016/j.tics.2020.09.004 ↗
13. A. Awasthi, S. Sarawagi, M. Assoc Comp, Continual Learning with Neural Networks: A Review, in: ACM India Joint International Conference on Data Science and Management of Data (CoDS-COMAD), Kolkata, INDIA, 2019, pp. 362-365. Doi: 10.1145/3297001.3297062.10.1145/3297001.3297062 ↗
14. An Appraisal of Incremental Learning Methods10.3390/e22111190 ↗
15. Wang · 2024
16. Corrosion-Fatigue Life Prediction for 2024–T62 Aluminum Alloy Using Damage Mechanics-Based Approach10.1177/1056789511432791 ↗
17. Incremental supervised learning: algorithms and applications in pattern recognition10.1007/s12065-019-00203-y ↗
18. Overcoming catastrophic forgetting in neural networks10.1073/pnas.1611835114 ↗
19. Homeostasis-Inspired Continual Learning: Learning to Control Structural Regularization10.1109/ACCESS.2021.3050176 ↗
20. Learning without forgetting10.1109/TPAMI.2017.2773081 ↗
21. Experience replay for continual learning
22. An artificial neural network-based algorithm for evaluation of fatigue crack propagation considering nonlinear damage accumulation10.3390/ma9060483 ↗
23. Prediction of the diagrams of fatigue fracture of D16T aluminum alloy by the methods of machine learning10.1007/s11003-018-0189-9 ↗
24. Prediction of fatigue crack propagation lives based on machine learning and data-driven approach
25. Prediction of fatigue–crack growth with neural network-based increment learning scheme10.1016/j.engfracmech.2020.107402 ↗
26. Factors controlling crack velocity in 7000 series aluminium alloys during fatigue in an aggressive environment10.1016/0010-938X(83)90117-8 ↗
27. The influence of loading frequency on the fatigue crack propagation behaviour of Al-Zn-Mg alloy at low cyclic stress intensity level in 3.5 wt.% NaCl solution: Chun, Y.-G., Pyun, S.-I and Lee, S.-M. J. Mater. Sci. Lett.10 24 (15 Dec 1991) 1439–1442, International Journal of Fatigue, 14 (1992) 413. .10.1007/BF00724400 ↗
28. Effect of a salt-water fog on fatigue crack growth behaviour of 7050 aluminium alloy in different orientations: Gurbuz, R., Doruk, M. and Schutz, W. J. Mater. Sci. Feb. 1991 26, (4), 1032–1038, International Journal of Fatigue, 13 (1991) 503. Doi: 10.1016/0142-1123(91)90523-2.10.1016/0142-1123(91)90523-2 ↗
29. Corrosion fatigue behavior of 7050 aluminum alloys in different tempers10.1016/S0013-7944(97)00173-2 ↗
30. Modeling of corrosion fatigue in metals in an aggressive environment10.1016/S0142-1123(01)00139-6 ↗
31. Acoustic emission study of corrosion fatigue crack propagation mechanism for LY12CZ and 7075–T6 aluminum alloys10.1007/s10853-005-1300-9 ↗
32. Some studies on fatigue crack growth rate of aluminum alloy 606110.1016/j.jmatprotec.2007.11.302 ↗
33. Influence of frequency and exposure to a saline solution on the corrosion fatigue crack growth behavior of the aluminum alloy 202410.1016/j.ijfatigue.2009.02.033 ↗
34. Fatigue crack propagation in 7050–T7451 plate alloy10.1016/j.engfracmech.2009.01.006 ↗
35. Investigation on corrosion fatigue crack growth rate in 7075 aluminum alloy10.1016/j.matdes.2013.04.097 ↗
36. Fatigue crack initiation and growth of aluminum alloy with stress ratio effects
37. Effect of environment on fatigue crack propagation behavior of an Al-Cu-Mg Aluminum alloy
38. Fatigue and crack-growth analyses on 7075–T651 aluminum alloy coupons under constant- and variable-amplitude loading10.1016/j.ijfatigue.2013.04.020 ↗
39. Influence of grain structure and crystallographic orientation on fatigue crack propagation behavior of 7050 alloy thick plate10.1016/j.ijfatigue.2014.03.009 ↗
40. Fatigue Surface Crack Growth in Aluminum Alloys under Different Temperatures10.1016/j.proeng.2016.08.881 ↗
41. Fatigue crack propagation of aeronautic AA7050-T7451 and AA2050-T84 aluminum alloys in air and saline environments10.1016/j.ijfatigue.2021.106519 ↗
42. A Data-Driven Health Prognostics Approach for Steam Turbines Based on XGBoost and DTW10.1109/ACCESS.2019.2927488 ↗
43. A comparative analysis of gradient boosting algorithms10.1007/s10462-020-09896-5 ↗
44. Extreme gradient boosting (XGBoost) model to predict the groundwater levels in Selangor Malaysia10.1016/j.asej.2020.11.011 ↗
45. Data adjusting strategy and optimized XGBoost algorithm for novel insider threat detection model10.1016/j.jfranklin.2023.09.004 ↗
46. Shyam · Competitive Analysis of the Top Gradient Boosting Machine Learning Algorithms · 2020
47. Continual learning with deep generative replay
48. On tiny episodic memories in continual learning. arXiv
49. An evaluation of machine learning in algorithm selection for search problems10.3233/AIC-2012-0533 ↗
50. Remaining fatigue life assessment of corroded mooring chains using crack growth modelling10.1016/j.marstruc.2023.103446 ↗
51. Machine Learning-Based predictions of crack growth rates in an aeronautical aluminum alloy10.1016/j.tafmec.2024.104278 ↗
52. Corrosion fatigue mechanism and life prediction of railway axle EA4T steel exposed to artificial rainwater10.1016/j.engfailanal.2022.106319 ↗
53. Corrosion fatigue life prediction for steel bar in concrete based on fatigue crack propagation and equivalent initial flaw size10.1016/j.conbuildmat.2018.11.056 ↗
54. H. Ritter, A. Botev, D. Barber, Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting, in: 32nd Conference on Neural Information Processing Systems (NIPS), Montreal, CANADA, 2018. Doi: 10.48550/arXiv.1805.07810.