Transferability of anomaly data to fatigue properties of PBF-LB AlSi10Mg parts with different volumes
Source abstract
Abstract
Fatigue of metallic materials produced by Laser Powder Bed Fusion has been extensively studied for both standard testing specimens and application relevant components. However, fatigue properties of specimens and components suffer from large scatter, mainly due to the presence of volumetric anomalies, residual stresses and surface roughness . Therefore, the transferability from specimens to components is still an open point that must be addressed. In this work, fatigue properties of notched components are investigated and compared with the standard specimens. Then, the properties of the components are inferred from the standard specimens’ data using simple models. The maxima anomalies’ distributions are estimated with a competing risk approach using Machine learning-assisted Extreme Value Statistics. Finally, the S-N curves of the investigated components, predicted employing the Shiozawa model for finite life and the El-Haddad model for the fatigue limit, closely matched the experimental results.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Aluminum Alloy Microstructure Properties · Additive Manufacturing Materials and Processes · Welding Techniques and Residual Stresses
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (48)
Show source-provided references
1. Additive manufacturing of fatigue resistant materials: Challenges and opportunities10.1016/j.ijfatigue.2017.01.001 ↗
2. Fatigue properties of AlSi10Mg obtained by additive manufacturing: Defect-based modelling and prediction of fatigue strength10.1016/j.engfracmech.2017.11.002 ↗
3. 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 ↗
4. Defects in additive manufactured metals and their effect on fatigue performance: A state-of-the-art review10.1016/j.pmatsci.2020.100724 ↗
5. Structural integrity of additively manufactured aluminum alloys: Effects of build orientation on microstructure, porosity, and fatigue behavior
6. 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 ↗
7. Murakami · 2019
8. Effects of defects in laser additive manufactured Ti-6Al-4V on fatigue properties10.1016/j.phpro.2014.08.120 ↗
9. A comparison of fatigue strength sensitivity to defects for materials manufactured by AM or traditional processes10.1016/j.ijfatigue.2016.06.020 ↗
10. Statistical estimation of fatigue design curves from datasets involving failures from defects10.1016/j.ijfatigue.2023.107882 ↗
11. On the role of melt flow into the surface structure and porosity development during selective laser melting10.1016/j.actamat.2015.06.004 ↗
12. Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction10.1038/s41598-017-03761-2 ↗
13. In situ X-ray imaging of defect and molten pool dynamics in laser additive manufacturing10.1038/s41467-018-03734-7 ↗
14. A review of selective laser melting of aluminum alloys: Processing, microstructure, property and developing trends10.1016/j.jmst.2018.09.004 ↗
15. International · 2020
16. ECSS Secretariat · 2021
17. X-ray CT analysis of the influence of process on defect in Ti-6Al-4V parts produced with selective laser melting technology10.1007/s00170-019-04347-0 ↗
18. Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights10.1016/j.matdes.2019.108385 ↗
19. Quality control of AlSi10Mg produced by SLM: Metallography versus CT scans for critical defect size assessment
20. Defect characteristics and analysis of their variability in metal L-PBF additive manufacturing10.1016/j.matdes.2019.108091 ↗
21. The effect of defect population on the anisotropic fatigue resistance of AlSi10Mg alloy fabricated by laser powder bed fusion10.1016/j.ijfatigue.2021.106317 ↗
22. A statistical approach to determine data requirements for part porosity characterization in laser powder bed fusion additive manufacturing10.1016/j.matchar.2022.112027 ↗
23. On the probabilistic prediction for extreme geometrical defects induced by laser-based powder bed fusion10.1016/j.cirpj.2022.11.024 ↗
24. Inclusion rating by statistics of extreme values and its application to fatigue strength prediction and quality control of materials10.6028/jres.099.032 ↗
25. More than 25 years of extreme value statistics for defects: Fundamentals, historical developments, recent applications10.1016/j.ijfatigue.2021.106407 ↗
26. Machine learning-assisted extreme value statistics of anomalies in AlSi10Mg manufactured by L-PBF for robust fatigue strength predictions10.1016/j.matdes.2023.112392 ↗
27. Feature-based volumetric defect classification in metal additive manufacturing10.1038/s41467-022-34122-x ↗
28. Machine learning augmented X-ray computed tomography features for volumetric defect classification in laser beam powder bed fusion10.1007/s00170-023-11281-9 ↗
29. Towards material and process agnostic features for the classification of pore types in metal additive manufacturing10.1016/j.matdes.2023.111757 ↗
30. Defect classification for additive manufacturing with machine learning10.3390/ma16186242 ↗
31. Fatigue behaviour of additively manufactured Ti-6Al-4V alloy: The role of defects on scatter and statistical size effect10.1016/j.ijfatigue.2020.105811 ↗
32. Impact of melt pool geometry variability on lack-of-fusion porosity and fatigue life in powder bed fusion-laser beam Ti–6Al–4V
33. Observation of spatter-induced stochastic lack-of-fusion in laser powder bed fusion using in situ process monitoring
34. Moisture effects on qualities and properties of laser powder bed fusion (LPBF) additive manufacturing of as-built 17-4PH stainless steel parts10.3390/met13091550 ↗
35. 2021
36. A benchmark activity on the fatigue life assessment of AlSi10Mg components manufactured by L-PBF10.1016/j.matdes.2022.110713 ↗
37. Extreme value models for the assessment of steels containing multiple types of inclusion10.1016/j.actamat.2006.01.016 ↗
38. Reiss · 1997
39. Beretta · 2010
40. Coles · 2001
41. Qualification of AM parts: Extreme value statistics applied to tomographic measurements10.1016/j.matdes.2017.05.091 ↗
42. Material defects as the basis of fatigue design10.1016/j.ijfatigue.2011.12.001 ↗
43. Fatigue life prediction of additively manufactured material: Effects of surface roughness, defect size, and shape10.1111/ffe.12799 ↗
44. Effect of non-metallic inclusion size and residual stresses on gigacycle fatigue properties in high strength steel10.4028/www.scientific.net/AMR.44-46.33 ↗
45. Uniform fatigue damage tolerance assessment for additively manufactured and cast Al-Si alloys: An elastic-plastic fracture mechanical approach
46. Qualification of uniform fatigue damage tolerance law for additively manufactured and cast Al-Si alloys10.1016/j.prostr.2022.12.040 ↗
47. Analysis of fatigue strength of L-PBF AlSi10Mg with different surface post-processes: Effect of residual stresses10.3390/met12060898 ↗
48. Zio · 2013