About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Applications of Uncertainty Quantification (UQ) in Science and Engineering
|
| Presentation Title |
Leveraging Archival Additively Manufacturing Fatigue Data to Investigate the Role of Processing Porosity with Greater Precision |
| Author(s) |
Ian J. Wietecha-Reiman, Todd Palmer |
| On-Site Speaker (Planned) |
Ian J. Wietecha-Reiman |
| Abstract Scope |
Materials databases continue to be developed for additive manufacturing (AM) processes, and will eventually support the construction of design criteria, but are hindered in the near-term by process complexity. However, with advances in data mining and curation protocols, these efforts can be readily supplemented by the evaluation of archival datasets through a meta-analysis to quantify the marginal contributions to scatter with a greater level of precision and sensitivity that would otherwise not be capable for most isolated studies. This approach enable a more nuanced investigation of the roles of multiple material and processing parameters and it can also identify relatively small effects. When applied to AM Ti-6Al-4V fatigue, this approach can enable a broader investigation of the role of processing porosity by incorporating quantitative fractography. |