About this Abstract |
Meeting |
2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
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Symposium
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2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
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Presentation Title |
Digitally Twinned Additive Manufacturing: Real-time Detection of Flaws in Laser Powder Bed Fusion by Combining Thermal Simulations with In-Situ Meltpool Sensor Data |
Author(s) |
Alex Riensche, Reza Yavari, Emine Tekerek, Lars Jacquemetton, Harold (Scott) Halliday, Ziyad Smoqi, Vignesh Perumal, Antonios Kontsos, Kevin Cole, Prahalad K. Rao |
On-Site Speaker (Planned) |
Prahalad K. Rao |
Abstract Scope |
The goal of this research is the real-time detection of incipient flaw formation in metal parts made using the laser powder bed fusion (LPBF) additive manufacturing process. Another emerging concern in LPBF, and additive manufacturing in general, is related to cyber security – malicious actors may tamper with the process or plant flaws inside a part to compromise its performance. The objective of this work is to develop and apply a physics and data integrated strategy to detect incipient flaw formation in LPBF parts. The approach is based on combining (twinning) real-time in-situ meltpool temperature measurements with a computationally efficient graph theory-based thermal simulation model predicts the thermal history. The temperature distribution predictions provided by the computational thermal model are updated with real-time meltpool temperature measurements. This digital twin approach is applied to detect flaw formation in stainless steel (316L) impeller-shaped parts made using a commercial LPBF system. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |