| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Fatigue and Fracture IV: Toward Confident Use in Critical Applications
|
| Presentation Title |
Flaw Identification in Additively Manufactured Components: Capabilities and Limitations |
| Author(s) |
Griffin Jones, Rachel Reed, Jayme S. Keist, Zackary Snow, Veeraraghavan Sundar |
| On-Site Speaker (Planned) |
Jayme S. Keist |
| Abstract Scope |
In additive manufacturing (AM), internal flaws that form during processing can have a detrimental impact on the resulting fatigue behavior of the component. Nondestructive X-ray computed tomography (CT) has been routinely used to inspect AM components. This technique, however, is limited by what is resolvable as well as the automated procedures available to analyze the data. In this study, we compared X-ray CT scans and automated defect recognition (ADR) analysis of the data to results obtained from an automated mechanical-polishing based serial sectioning system. Although the internal porosity, microcracks and surface roughness were easily observed by serial sectioning with bright field optical microscopy, the same level information could not be obtained from the X-ray CT data or from the automated defect recognition (ADR) algorithms. The results point to the limitations of X-ray CT as well as highlighting the need for further ADR development for flaw identification in AM components. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |