About this Abstract |
Meeting |
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
Symposium
|
Additive Manufacturing Benchmarks 2022 (AM-Bench 2022)
|
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) |
Reza Yavari, Prahalada K. Rao, Alex Riensche, Emine Tekerek, Lars Jacquemetton, Ziyad Smoqi, Vignesh Perumal, Antonios Kontsos, Harold (Scott) Halliday, Kevin Cole |
On-Site Speaker (Planned) |
Prahalada K. Rao |
Abstract Scope |
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. This digital twin approach is applied to detect flaw formation in stainless steel (316L) impeller-shaped parts. Four such impellers are produced emulating three pathways of flaw formation in LPBF parts, these are: changes in the processing parameters (process drifts); machine-related malfunctions, and deliberate tampering with the process to plant flaws inside the part (cyber intrusions). The digital twin approach is shown to be effective for detection of the three types of flaw formation causes studied in this work. |
Proceedings Inclusion? |
Undecided |