Fatigue-life prediction of additively manufactured metals by continuous damage mechanics (CDM)-informed machine learning with sensitive features
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 · Additive Manufacturing and 3D Printing Technologies · Advanced Machining and Optimization Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (42)
Show source-provided references
1. Machinability of additively manufactured titanium alloys: A comprehensive review10.1016/j.jmapro.2022.01.007 ↗
2. The roles and applications of additive manufacturing in the aerospace and automobile sector
3. Advances in coaxial additive manufacturing and applications
4. Defect-induced fatigue scattering and assessment of additively manufactured 300M-AerMet100 steel: An investigation based on experiments and machine learning10.1016/j.engfracmech.2022.108352 ↗
5. Multiaxial thermo-mechanical fatigue life prediction based on notch local stress-strain estimation considering temperature change10.1016/j.engfracmech.2022.108384 ↗
6. A modified stress field intensity approach for fatigue life prediction of components10.1016/j.matdes.2020.108537 ↗
7. Multiaxial fatigue assessment for outer cylinder of landing gear by critical plane method10.1177/09544100211026042 ↗
8. An efficient fatigue and creep-fatigue life prediction method by using the hysteresis energy density rate concept10.1111/ffe.13230 ↗
9. Foreign object damage tolerance and fatigue analysis of induction hardened S38C axles10.1016/j.matdes.2021.109488 ↗
10. Prediction and evaluation of fatigue life via modified energy method considering surface processing10.1177/10567895211045120 ↗
11. Assessment of notch fatigue and size effect using stress field intensity approach10.1016/j.ijfatigue.2021.106279 ↗
12. Defect tolerant fatigue assessment of AM materials: Size effect and probabilistic prospects10.1016/j.ijfatigue.2022.106884 ↗
13. 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 ↗
14. Material defects as the basis of fatigue design10.1016/j.ijfatigue.2011.12.001 ↗
15. Fatigue life and mechanistic modeling of interior micro- defect induced cracking in high cycle and very high cycle regimes10.1016/j.actamat.2018.07.036 ↗
16. Fatigue life evaluation of Ti-6Al-4V welded joints manufactured by electron beam melting10.1111/ffe.13490 ↗
17. A machine learning approach for the prediction of melting efficiency in wire arc additive manufacturing10.1007/s00170-022-08966-y ↗
18. A deep learning based life prediction method for components under creep, fatigue and creep-fatigue conditions10.1016/j.ijfatigue.2021.106236 ↗
19. Smart manufacturing must embrace big data10.1038/544023a ↗
20. A dimensionally augmented and physics-informed machine learning for quality prediction of additively manufactured high-entropy alloy10.1016/j.jmatprotec.2022.117637 ↗
21. Machine learning based predictions of fatigue crack growth rate of additively manufactured Ti6Al4V
22. A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures10.1016/j.engfracmech.2021.108130 ↗
23. Prediction of greenhouse gas emissions reductions via machine learning algorithms: Toward an artificial intelligence-based life cycle assessment for automotive lightweighting
24. Machine-learning assisted laser powder bed fusion process optimization for AlSi10Mg: new microstructure description indices and fracture mechanisms10.1016/j.actamat.2020.10.010 ↗
25. Online quality inspection using Bayesian classification in powder-bed additive manufacturing from high-resolution visual camera images10.1007/s10845-018-1412-0 ↗
26. The potency of defects on fatigue of additively manufactured metals10.1016/j.ijmecsci.2022.107185 ↗
27. Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning
28. Machine learning based very-high-cycle fatigue life prediction of Ti-6Al-4V alloy fabricated by selective laser melting10.1016/j.ijfatigue.2022.106764 ↗
29. Experimental investigation on shear strength of laser powder bed fusion AlSi10Mg under quasi-static and dynamic loads
30. Effects of build orientation and heat treatments on the tensile and fracture toughness properties of additively manufactured AlSi10Mg10.1016/j.ijmecsci.2021.106868 ↗
31. A review on the fatigue behaviour of AlSi10Mg alloy fabricated using laser powder bed fusion technique10.1016/j.jmrt.2022.01.028 ↗
32. Experimental and numerical mechanical characterization of additively manufactured Ti6Al4V lattice structures considering progressive damage10.1016/j.ijmecsci.2020.105986 ↗
33. The microstructure and mechanical properties of the additive manufactured AlCoCrFeNi high entropy alloy10.1016/j.msea.2021.142507 ↗
34. Fatigue behaviour of notched laser powder bed fusion AlSi10Mg after thermal and mechanical surface post-processing10.1016/j.msea.2021.142145 ↗
35. On the efficiency of machine learning for fatigue assessment of post-processed additively manufactured AlSi10Mg10.1016/j.ijfatigue.2022.106841 ↗
36. Stable learning establishes some common ground between causal inference and machine learning10.1038/s42256-022-00445-z ↗
37. Murakami · 2012
38. Development of a novel fatigue damage model with AM effects for life prediction of commonly-used alloys in aerospace10.1016/j.ijmecsci.2019.02.032 ↗
39. Influence of processing parameters of selective laser melting on high-cycle and very-high-cycle fatigue behaviour of Ti-6Al-4V10.1111/ffe.13361 ↗
40. Theory-guided data science: A new paradigm for scientific discovery from data10.1109/TKDE.2017.2720168 ↗
41. Defect criticality analysis on fatigue life of L-PBF 17–4 PH stainless steel via machine learning10.1016/j.ijfatigue.2022.107018 ↗
42. Fatigue life prediction considering mean stress effect based on random forests and kernel extreme learning machine10.1016/j.ijfatigue.2022.106761 ↗