Machine learning based very-high-cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting
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: Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (45)
Show source-provided references
1. Additive manufacturing of metals10.1016/j.actamat.2016.07.019 ↗
2. In-situ SEM investigation on fatigue behaviors of additive manufactured Al-Si10-Mg alloy at elevated temperature10.1016/j.engfracmech.2019.03.040 ↗
3. Additive manufacturing of metallic materials: a review10.1007/s11665-017-2747-y ↗
4. The nature and the mechanism of crack initiation and early growth for very-high-cycle fatigue of metallic materials – an overview10.1016/j.tafmec.2017.05.002 ↗
5. Very-high-cycle fatigue behavior of AlSi10Mg manufactured by selective laser melting: Effect of build orientation and mean stress10.1016/j.ijfatigue.2020.105696 ↗
6. Microstructure evolution and very-high-cycle fatigue crack initiation behavior of a structural steel with two loading intermittence modes10.1016/j.ijfatigue.2022.106904 ↗
7. Evaluation of fatigue and creep-fatigue damage levels on the basis of engineering damage mechanics approach10.1016/j.ijfatigue.2022.107277 ↗
8. Additive manufacturing of Ti6Al4V alloy: a review10.1016/j.matdes.2018.107552 ↗
9. Correlation between porosity and processing parameters in TiAl6V4 produced by selective laser melting10.1016/j.matdes.2016.05.070 ↗
10. Defect criticality analysis on fatigue life of L-PBF 17–4 PH stainless steel via machine learning10.1016/j.ijfatigue.2022.107018 ↗
11. Defect tolerant fatigue assessment of AM materials: size effect and probabilistic prospects10.1016/j.ijfatigue.2022.106884 ↗
12. A review on the fatigue behaviour of AlSi10Mg alloy fabricated using laser powder bed fusion technique10.1016/j.jmrt.2022.01.028 ↗
13. Fatigue failure mechanisms for AlSi10Mg manufactured by L-PBF under axial and torsional loads: the role of defects and residual stresses10.1016/j.ijfatigue.2022.106903 ↗
14. Very-high-cycle fatigue behavior of Ti-6Al-4V manufactured by selective laser melting: effect of build orientation10.1016/j.ijfatigue.2020.105628 ↗
15. Artificial intelligence assisted fatigue failure prediction10.1016/j.ijfatigue.2021.106580 ↗
16. Artificial intelligence modeling of ultrasonic fatigue test to predict the temperature increase10.1016/j.ijfatigue.2022.106999 ↗
17. Machine learning-based real-time visible fatigue crack growth detection10.1016/j.dcan.2021.03.003 ↗
18. Application of deep neural network to predict the high-cycle fatigue life of AISI 1045 steel coated by industrial coatings10.3390/jmse10020128 ↗
19. Fatigue modeling using neural networks: a comprehensive review10.1111/ffe.13640 ↗
20. New approaches for a reliable fatigue life prediction of powder metallurgy components using machine learning10.1111/ffe.13921 ↗
21. Effect of the porosity on the fatigue strength of metals10.1111/ffe.13783 ↗
22. Comparison of machine learning and stress concentration factors-based fatigue failure prediction in small-scale butt-welded joints10.1111/ffe.13800 ↗
23. Low-cycle fatigue parameters and fatigue life estimation of high-strength steels with artificial neural networks10.1111/ffe.13847 ↗
24. A fatigue life prediction approach for laser-directed energy deposition titanium alloys by using support vector regression based on pore-induced failures10.1016/j.ijfatigue.2022.106748 ↗
25. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: New microstructure description indices and fracture mechanisms10.1016/j.actamat.2020.10.010 ↗
26. On the efficiency of machine learning for fatigue assessment of post-processed additively manufactured AlSi10Mg10.1016/j.ijfatigue.2022.106841 ↗
27. The potency of defects on fatigue of additively manufactured metals10.1016/j.ijmecsci.2022.107185 ↗
28. Ultrasonic fatigue of laser beam powder bed fused metals: a state-of-the-art review10.1016/j.engfailanal.2021.106015 ↗
29. Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting10.1016/j.ijfatigue.2022.106764 ↗
30. A virtual sample generation algorithm supporting machine learning with a small-sample dataset: a case study for rubber materials10.1016/j.commatsci.2022.111475 ↗
31. Application of tabular data synthesis using generative adversarial networks on machine learning-based multiaxial fatigue life prediction10.1016/j.ijpvp.2022.104779 ↗
32. Review on the correlation between microstructure and mechanical performance for laser powder bed fusion AlSi10Mg
33. Very-high-cycle fatigue induced growth and amorphization of Si particles in additively manufactured AlSi10Mg alloy: dependence of applied stress ratio10.1016/j.ijfatigue.2022.107167 ↗
34. Defect-induced cracking and fine granular characteristics in very-high-cycle fatigue of laser powder bed fusion AlSi10Mg alloy10.1016/j.ijfatigue.2022.106770 ↗
35. Growth and stability of interacting surface flaws of arbitrary shape10.1016/0013-7944(83)90027-9 ↗
36. Effects of defect size and location on high cycle fatigue life of a maraging stainless steel at ambient and cryogenic temperatures10.1016/j.ijfatigue.2022.106906 ↗
37. Importance of input data normalization for the application of neural networks to complex industrial problems10.1109/23.589532 ↗
38. A strategy to apply machine learning to small datasets in materials science10.1038/s41524-018-0081-z ↗
39. Essential structure of S-N curve: prediction of fatigue life and fatigue limit of defective materials and nature of scatter10.1016/j.ijfatigue.2020.106138 ↗
40. The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis10.1016/S0304-4076(01)00113-0 ↗
41. Sakuma J, Kobayashi S. Non-parametric expectation-maximization for Gaussian mixtures. Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP ’02., vol. 1, Singapore: Nanyang Technol. Univ; 2002, p. 517–22. https://doi.org/10.1109/ICONIP.2002.1202224.10.1109/ICONIP.2002.1202224 ↗
42. Model selection strategies for identifying most relevant covariates in homoscedastic linear models10.1016/j.csda.2009.09.006 ↗
43. Experimental validation of an ANN model for random loading fatigue analysis10.1016/j.ijfatigue.2019.04.028 ↗
44. A survey on modern trainable activation functions10.1016/j.neunet.2021.01.026 ↗
45. Machine learning based fatigue life prediction with effects of additive manufacturing process parameters for printed SS 316L10.1016/j.ijfatigue.2020.105941 ↗