Fatigue life prediction of aluminum alloy via knowledge-based machine learning
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OpenAlex topics: Fatigue and fracture mechanics · Non-Destructive Testing Techniques · Hydrogen embrittlement and corrosion behaviors in metals
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References (38)
Show source-provided references
1. Fatigue behavior of 2618–T851 aluminum alloy under uniaxial and multiaxial loadings10.1016/j.ijfatigue.2019.105322 ↗
2. Fatigue behavior of high-entropy alloys: A review10.1007/s11431-017-9137-4 ↗
3. The exponential law of endurance tests
4. Application of bi-linear log–log S-N model to strain-controlled fatigue data of aluminum alloys and its effect on life predictions10.1016/j.ijfatigue.2005.03.003 ↗
5. A probabilistic Stüssi function for modelling the S-N curves and its application on specimens made of steel S355J2+N10.1016/j.ijfatigue.2018.07.041 ↗
6. Estimating Fatigue Curves With the Random Fatigue-Limit Model10.1080/00401706.1999.10485925 ↗
7. Determination of fatigue life for low probability of failure for different stress levels using 3-parameter Weibull distribution10.1016/j.ijfatigue.2020.106080 ↗
8. An empirical model for fatigue behavior prediction of glass fibre-reinforced plastic composites for various stress ratios and test frequencies10.1016/S1359-835X(03)00052-6 ↗
9. A machine-learning fatigue life prediction approach of additively manufactured metals10.1016/j.engfracmech.2020.107508 ↗
10. Machine learning-based predictions of fatigue life and fatigue limit for steels10.1016/j.jmst.2021.02.021 ↗
11. Using machine learning and feature engineering to characterize limited material datasets of high-entropy alloys10.1016/j.commatsci.2020.109618 ↗
12. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters10.1186/2193-9772-3-8 ↗
13. Deep materials informatics: Applications of deep learning in materials science10.1557/mrc.2019.73 ↗
14. A novel method of multiaxial fatigue life prediction based on deep learning10.1016/j.ijfatigue.2021.106356 ↗
15. Machine learning-based genetic feature identification and fatigue life prediction10.1111/ffe.13532 ↗
16. Pore-affected fatigue life scattering and prediction of additively manufactured Inconel 718: An investigation based on miniature specimen testing and machine learning approach10.1016/j.msea.2020.140693 ↗
17. A unified approach to interpreting model predictions
18. Alloy and Temper Designation Systems for Aluminum and Aluminum Alloys
19. Fatigue life scatter in 7xxx series aluminum alloys10.1016/j.ijfatigue.2009.07.016 ↗
20. Microstructural characterization and high cycle fatigue behavior of investment cast A357 aluminum alloy10.1016/j.ijfatigue.2015.03.004 ↗
21. High cycle fatigue strength of permanent mold and rheocast aluminum 357 alloy10.1016/j.ijfatigue.2010.01.001 ↗
22. Fatigue of 2024–T351 aluminium alloy at different load ratios up to 1010 cycles10.1016/j.ijfatigue.2012.07.013 ↗
23. NIMS 2020. https://smds.nims.go.jp/MSDS/en/sheet/Fatigue.html#18.
24. MakeitFrom 2020. https://www.makeitfrom.com/material-group/Aluminum-Alloy.
25. A strategy to apply machine learning to small datasets in materials science10.1038/s41524-018-0081-z ↗
26. A Survey on Transfer Learning10.1109/TKDE.2009.191 ↗
27. Toward Optimal Feature Selection
28. Random forests10.1023/A:1010933404324 ↗
29. Greedy function approximation: A gradient boosting machine10.1214/aos/1013203451 ↗
30. Scikit-learn: Machine learning in Python
31. An online tool for predicting fatigue strength of steel alloys based on ensemble data mining10.1016/j.ijfatigue.2018.04.017 ↗
32. Effect of Zr on the high cycle fatigue and mechanical properties of Al–Si–Cu–Mg alloys at elevated temperatures10.1016/j.jallcom.2019.151795 ↗
33. Comparison of genetic programming with conventional methods for fatigue life modeling of FRP composite materials10.1016/j.ijfatigue.2007.11.007 ↗
34. Effect of humidity on porosity, microstructure, and fatigue strength of A7N01S-T5 aluminum alloy welded joints in high-speed trains10.1016/j.matdes.2015.06.177 ↗
35. Fatigue damage evolution and lifetime prediction of welded joints with the consideration of residual stresses and porosity10.1016/j.ijfatigue.2017.06.014 ↗
36. Residual stress effects on fatigue life prediction using hardness measurements for butt-welded joints made of high strength steels10.1016/j.ijfatigue.2021.106175 ↗
37. Design of high-entropy alloys with a single solid-solution phase: Average properties vs. their variances10.1016/j.jallcom.2018.01.252 ↗
38. Fatigue crack propagation in heterogeneous materials under remote cyclic loading10.1142/S242491301650003X ↗