| About this Abstract |
| Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
| Presentation Title |
Use of Extreme Value Analysis to Determine Data Requirements for Defect Characterization and Predict Variation in Fatigue Performance |
| Author(s) |
Tharun Reddy, Mahya Shahabi, David Scannapieco, Austin Ngo, Anthony Rollett, John Lewandowski, Sneha Prabha Narra |
| On-Site Speaker (Planned) |
Sneha Prabha Narra |
| 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 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 yields the best predictions. An analysis framework is used to demonstrate applicability for predicting critical pore size in fatigue samples and compare to initiating defect size and defect density on the fracture surface. |
| Proceedings Inclusion? |
Undecided |