| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Presentation Title |
Developing a Practical In-situ AM Process Monitoring Framework to Reduce Qualification Burdens |
| Author(s) |
Michael Heiden, Dan Bolintineanu, Anthony Garland, David Moore, David Saiz, Tyler LeBrun, Ben Brown, Nick Calta |
| On-Site Speaker (Planned) |
Michael Heiden |
| Abstract Scope |
In-situ monitoring’s ability to evaluate metal AM processes for abnormal build events and detect detrimental defects have been demonstrated in laboratory settings. However, the challenge remains to convert these high-fidelity research and development frameworks into production-ready environments with deployable sensor hardware. The slow, resource-intensive nature associated with acquiring/analyzing large datasets with complex outputs remains a barrier toward reducing burdensome testing/inspection for qualification. There is also a need for software solutions that provide manageable amounts of practical data for decision-making. This presentation highlights an ongoing, multi-year effort within the DOE labs to develop a resource-effective in-situ instrumentation toolset for AM production environments, which leverages machine learning to form a common data processing framework. Discussion will cover how this framework aims to assist AM process development, ensure process consistency, and contribute to part acceptance for metal AM production.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |