| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Simulations/Experiments Integration for Next Generation Hypersonic Materials
|
| Presentation Title |
Accelerating a Digital Twin of Direct Energy Deposition Additive Manufacturing |
| Author(s) |
Saad Khairallah |
| On-Site Speaker (Planned) |
Saad Khairallah |
| Abstract Scope |
A multi-scale high fidelity model is developed to simulate the process of additively manufacturing small coupon parts in direct energy deposition. The model accounts for powder transport from the coaxial nozzle to the work piece as well as the effect of the carrier gas. Full laser ray tracing model is used to preheat the flying powder particles and to create the melt pool. Furthermore, microstructural cellular automata finite element analysis is carried out to determine the grain size distribution and orientation upon solidification. The high cost of modeling is brought down by using deep learning neural network as well as data driven reduced order modeling. The model is preliminarily applied to study several Titanium based alloys and to compare resulting microstructure and mechanical performance. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under contract DE- AC52-07NA27344. Lawrence Livermore National Security, LLC. LLNLABS-837562. |
| Proceedings Inclusion? |
Planned: |