About this Abstract |
Meeting |
2020 TMS Annual Meeting & Exhibition
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Symposium
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Additive Manufacturing: Advanced Characterization with Synchrotron, Neutron, and In Situ Laboratory-scale Techniques
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Presentation Title |
Quantifying Defect Signatures in Metal Additive Manufacturing Using In-situ Diagnostics |
Author(s) |
Manyalibo Matthews, Bradley Jared, John Carpenter, Elena Garlea, Benjamin Brown |
On-Site Speaker (Planned) |
Manyalibo Matthews |
Abstract Scope |
Prediction of process-structure-property relationships is a significant challenge that must be overcome to facilitate the rapid, widescale adoption of metal additive manufacturing (AM) for operation-critical applications. Specifically, on-line defect characterization and control remains elusive. To address these issues, in situ techniques that probe and clarify the physics of defect formation are required, along with diagnostics suitable for on-line process monitoring. Here we present studies of metal AM process signatures that have been monitored in-situ using pyrometry, imaging and optical coherence tomography (OCT). These signatures are correlated to as-built material structures captured using X-ray computed tomography (CT), radiography and metallography. Probability relations for defect detection were constructed based on pyrometer signals. Machine learning algorithms were also used to analyze thermal emission data and predict surface roughness. Resulting process-structure-property correlations will be discussed, along with their relevance to part qualification and production acceptance. Prepared by LLNL under Contract DE-AC52-07NA27344. |
Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |