About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing: Advanced Characterization With Synchrotron, Neutron, and In Situ Laboratory-scale Techniques IV
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| Presentation Title |
Enhancing Defect Detection in Additive Manufacturing Using In Situ Thermal Imaging and Acoustic Data |
| Author(s) |
Andrew Polonsky, Constantin Brif, Kyle Kotanchek, Paul Chao, Xiaona Zhou, Ian Halim, James Craig, Alan Abul-Haj |
| On-Site Speaker (Planned) |
Andrew Polonsky |
| Abstract Scope |
This study investigates the application of thermal imaging for defect detection in the additive manufacturing of 316L stainless steel, focusing on the influence of processing parameters on defect populations and their correlation with mechanical properties. We employed a combination of in situ thermal imaging and acoustic data collection to identify and characterize defects during the build process. The thermal imaging data was benchmarked against ex situ micro-computed tomography characterization to validate defect detection accuracy. Additionally, we developed models to understand melt pool geometries, which informed dimensionless energy input approaches, facilitating a materials-agnostic understanding of process parameters. By correlating in situ acoustic data with detected defects, we aim to enhance predictive capabilities for defect formation. This integrated approach not only improves defect detection but also provides insights into optimizing processing conditions to enhance the mechanical performance of additively manufactured components. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Characterization, |