| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Fatigue and Fracture: Towards Accurate Prediction
|
| Presentation Title |
NDE for Fatigue Assessment: a Study on the Anomaly Detection by X-CT |
| Author(s) |
Stefano Beretta, Shaharyar Baig, Alireza Jam, Shuai Shao, Nima Shamsaei |
| On-Site Speaker (Planned) |
Stefano Beretta |
| Abstract Scope |
The integration of X-ray Computed Tomography (X-CT) into non-destructive evaluation (NDE) for additively manufactured (AM) parts not only facilitates the detection and characterization of defects, but it is essential part of the part quality assurance.
AlSi10Mg samples were manufactured by laser-powder bed fusion with different diameters. They were subsequently scanned at different voxel sizes (3/6/10 microns) and the results of the detection were analyzed by taking the 3 micron detection as the 'ground-truth' to evaluate the performance at the different resolutions.The data was analyzed to determine how the probability of detection (PoD) is affected by X-CT parameters (voxel size and specimen diameter) and to highlight the significant differences in detection due to the morphology of defects. While the largest undetected defects were appropriate measures for the set-up of an X-CT detection procedure, the 'sizing error' was found to be critical for the quality assessment of AM parts. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Aluminum, |