|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||Additive Manufacturing: Advanced Characterization with Synchrotron, Neutron, and In Situ Laboratory-scale Techniques II
||NOW ON-DEMAND ONLY - Digitally Twinned Additive Manufacturing: Real-time Detection of Flaws in Laser Powder Bed Fusion by Combining Thermal Simulations with In-Situ Meltpool Sensor Data.
||Reza Yavari, Alex Riensche, Emine Tekerek, Adriane Tenequer , Lars Jacquemetton, Scott Halliday, Marcie Vandever, Ziyad Smoqi, Vignesh Perumal, Kevin Cole, Antonios Kontsos, Prahalad K. Rao
|On-Site Speaker (Planned)
||Prahalad K. Rao
The objective of this work is to develop and apply a physics and data integrated strategy to detect incipient flaw formation in laser powder bed fusion (LPBF) parts. The approach used to realize this objective is based on combining (twinning) real-time in-situ meltpool temperature measurements with a graph theory-based thermal simulation model that rapidly predicts the part-level temperature distribution in the part (thermal history). The novelty of the approach is that 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.
||Additive Manufacturing, Modeling and Simulation, ICME