Workflow for fatigue life prediction of additive manufactured complex designs from powder bed fusion of Ti–6Al–4V
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OpenAlex topics: Additive Manufacturing Materials and Processes · Titanium Alloys Microstructure and Properties · Additive Manufacturing and 3D Printing Technologies
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References (48)
Show source-provided references
1. Additive manufacturing of Ti6Al4V alloy: A review10.1016/j.matdes.2018.107552 ↗
2. Fatigue of additive manufactured Ti-6Al-4V, Part II: The relationship between microstructure, material cyclic properties, and component performance10.1016/j.ijfatigue.2019.105363 ↗
3. Data-driven materials investigations: The next frontier in understanding and predicting fatigue behavior10.1007/s11837-018-2894-0 ↗
4. The exponential law of endurance tests
5. A rational analytic theory of fatigue
6. Tanaka · 1981
7. The physics of fatigue crack initiation10.1016/j.ijfatigue.2012.10.009 ↗
8. Analysis of Fatigue Indicator Parameters for Ti-6Al-4V microstructures using extreme value statistics in the transition fatigue regime10.1016/j.ijfatigue.2021.106441 ↗
9. Microtexture analysis and modeling of ambient fatigue and creep-fatigue damages in Ti-6Al-4V alloy10.1007/s11661-016-3869-9 ↗
10. Microstructure stability and damage mechanisms in an α/β Ti-6Al-4V-0.55 Fe alloy during low cycle dwell-fatigue at room temperature10.1016/j.ijfatigue.2021.106585 ↗
11. Creep-fatigue damage behavior of a titanium alloy at room temperature: Experiments and modeling10.1016/j.ijmecsci.2023.108135 ↗
12. Applications of data-driven approaches in prediction of fatigue and fracture
13. Data-driven Bayesian model-based prediction of fatigue crack nucleation in Ni-based superalloys10.1038/s41524-022-00727-5 ↗
14. Predicting the 3D fatigue crack growth rate of small cracks using multimodal data via Bayesian networks: In-situ experiments and crystal plasticity simulations10.1016/j.jmps.2018.03.007 ↗
15. An online tool for predicting fatigue strength of steel alloys based on ensemble data mining10.1016/j.ijfatigue.2018.04.017 ↗
16. Gaussian process for machine learning-based fatigue life prediction model under multiaxial stress–strain conditions10.3390/ma15217797 ↗
17. Data-driven, physics-based, or both: Fatigue prediction of structural adhesive joints by artificial intelligence10.3390/applmech4010019 ↗
18. Budynas · 2011
19. 2021
20. The influence of porosity on fatigue crack initiation in additively manufactured titanium components10.1038/s41598-017-06504-5 ↗
21. Surface stress concentration factor via Fourier representation and its application for machined surfaces10.1016/j.ijsolstr.2017.01.023 ↗
22. Quantitative evaluation of fatigue strength of metals containing various small defects or cracks10.1016/0013-7944(83)90018-8 ↗
23. Numerical modelling of surface roughness effect on the fatigue behavior of Ti-6Al-4V obtained by additive manufacturing10.1016/j.ijfatigue.2019.02.014 ↗
24. A new FE post-processor for probabilistic fatigue assessment in the presence of defects and its application to AM parts10.1016/j.ijfatigue.2019.04.008 ↗
25. Murakami · 2019
26. Influence of defects, surface roughness and HIP on the fatigue strength of Ti-6Al-4V manufactured by additive manufacturing10.1016/j.ijfatigue.2018.07.020 ↗
27. A comparison of fatigue strength sensitivity to defects for materials manufactured by AM or traditional processes10.1016/j.ijfatigue.2016.06.020 ↗
28. Notch effects in high cycle fatigue of Ti-6Al-4V
29. Fatigue strength of Ti-6Al-4V alloys containing small artificial defects10.2472/jsms.52.12Appendix_263 ↗
30. Effects of defects in laser additive manufactured Ti-6Al-4V on fatigue properties10.1016/j.phpro.2014.08.120 ↗
31. Fatigue properties of AlSi10Mg obtained by additive manufacturing: Defect-based modelling and prediction of fatigue strength10.1016/j.engfracmech.2017.11.002 ↗
32. Prediction of non propagating cracks10.1016/0013-7944(79)90081-X ↗
33. Representing crack growth in additively manufactured Ti-6Al-4V10.1016/j.ijfatigue.2018.07.019 ↗
34. A mechanistic approach to the Kitagawa–Takahashi diagram using a multiaxial probabilistic framework10.1016/j.engfracmech.2013.06.001 ↗
35. The effects of environment and load frequency on the crack propagation law for macro fatigue crack growth in aluminium alloys10.1016/0013-7944(70)90003-2 ↗
36. Application of the Hartman–Schijve equation to represent Mode I and Mode II fatigue delamination growth in composites10.1016/j.compstruct.2011.11.030 ↗
37. Computing the durability of WAAM 18Ni 250 maraging steel specimens10.1111/ffe.13828 ↗
38. A generalized Paris’ law for fatigue crack growth10.1016/j.jmps.2006.01.007 ↗
39. Fatigue performance of additive manufactured TiAl6V4 using electron and laser beam melting10.1016/j.ijfatigue.2016.05.001 ↗
40. 2023
41. 2023
42. 2023
43. Fatigue crack growth of electron beam melted Ti-6Al-4V in high-pressure hydrogen10.3390/ma13061287 ↗
44. Multiaxial fatigue behavior of wrought and additive manufactured Ti-6Al-4V including surface finish effect10.1016/j.ijfatigue.2017.03.044 ↗
45. Walker · 1970
46. Testing and evaluation for fatigue crack propagation of Ti-6Al-4V/ELI and 7050-T7452 alloys at high temperatures10.1016/j.cja.2017.06.013 ↗
47. Monte Carlo simulation of weldment fatigue strength10.1016/0143-974X(89)90060-6 ↗
48. Reliability-based fatigue life estimation of shear riveted connections considering dependency of rivet hole failures