| 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 |
Data Driven and High Fidelity Modeling Approaches to Advance Understanding and TRL Level of 3D Printing |
| Author(s) |
Saad Khairallah, Amit Kumar, Justin Patridge, Gabe Guss, Eric Chin, Youngsoo Choi, Joseph Mckeown |
| On-Site Speaker (Planned) |
Saad Khairallah |
| Abstract Scope |
A multi-scale ALE3D high fidelity model is developed to simulate directed energy deposition. The model captures the powder transport from the coaxial nozzle to the work piece as well as the effect of the carrier gas and laser ray tracing heating and reveals a new kind of air-cushioned. The high cost of modeling is brought down by using deep learning and data driven reduced order modeling at different scales. The end goal is to combine modeling with a data driven approach for “first time right” also referred to here as intelligent feedforward (IFF). We showcase how IFF is used to optimize laser power and scan speed to print complex large parts with overhang geometries and obtain high geometric accuracy.
Work 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. LLNL-ABS-854766 |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |