About this Abstract |
| Meeting |
MS&T26: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
A Low Cost Geometrically Driven Laser Powder Bed Fusion Microstructure Model |
| Author(s) |
Gregory D. Wong, Gregory S. Rohrer, Anthony D. Rollett |
| On-Site Speaker (Planned) |
Gregory D. Wong |
| Abstract Scope |
Laser powder bed fusion (LPBF) metals additive manufacturing is a transformative technology for manufacturing, but it also opens a very large design space in both process optimization and physical part design creating a large materials science challenge. This wide process parameter design space results in the need for computational tools to model and predict the process-structure linkage at low computational cost. This work presents the FLAME model, which is a low cost geometrically driven LPBF solidification microstructure model. By utilizing geometric assumptions for solidification and growth grain shapes and computational optimization, FLAME achieves an order of magnitude level efficiency improvements over higher cost alternatives even while using smaller scale non-HPC hardware. Results simulating as-printed microstructures for Ni superalloys and Ti-6Al-4V will be shown highlighting the multi-material applicability of the FLAME model. Finally, a surrogate driven approach using machine learning will be shown. |