| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Fatigue and Fracture: Developing Predictive Capabilities
|
| Presentation Title |
A Method to Predict Critical Pore/Defect Size in Laser Powder Bed Fusion Additively Manufactured Ti-6Al-4V Parts |
| Author(s) |
Mahya Shahabi, Austin Ngo, David Scannapieco, John Lewandowski, Sneha Prabha Narra |
| On-Site Speaker (Planned) |
David Scannapieco |
| Abstract Scope |
A major factor in the fatigue life of fracture-critical parts is the effect of process-induced defects and the critical pore/defect size. Prediction of critical pore/defect size in different process regimes of a laser powder bed fusion (L-PBF) processed part could provide invaluable information for the widening application of additive manufacturing. This study uses extreme value analysis to predict critical pore/defect size in Ti-6Al-4V bend bar samples using the 2D cross-sectional porosity data. The results confirm that the pore/defect density and the required model precision determine the data required to characterize part porosity, the maximum pore/defect size prediction from process conditions used for one sample applies to another sample with similar porosity distribution, and the peaks-over-threshold model predicts critical pore size reasonably well. An analysis framework is presented and is used to demonstrate its applicability to predict the critical pore size in fatigue samples, with comparison to fracture surface observations. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Titanium, Characterization |