About this Abstract |
Meeting |
2025 TMS Annual Meeting & Exhibition
|
Symposium
|
Additive Manufacturing Modeling, Simulation and Machine Learning
|
Presentation Title |
Sensing-Based AM Process Mapping to Improve Reliability |
Author(s) |
Glenn E. Bean |
On-Site Speaker (Planned) |
Glenn E. Bean |
Abstract Scope |
Adoption of additive manufacturing (AM) for complex metallic spaceflight applications is continuing to increase. Especially for large components that have a long build time or occupy a majority of the build volume, it is critical to understand process variation impacts on AM build quality. In some laser powder bed fusion (LPBF) systems, there is known variability within the build volume due to the interplay between laser parameters, cooling rates, position, and part section thickness. However, defects that result in degraded properties are not always visible and must be characterized by a combination of in-situ and ex-situ analysis. Data-driven characterization of AM parameter regimes can improve reliability of AM components by measuring defects and predicting impact on performance. This work summarizes progress on producing computation- and sensor-connected process maps with a focus on defect identification and microstructure characterization. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, ICME, Aluminum |