| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Late News Poster Session
|
| Presentation Title |
A-71: Microstructure Prediction in Powder Bed Metal Additive Manufacturing Using Coupled Nucleation and Monte Carlo Method |
| Author(s) |
Aashique Alam Rezwan, Theron Rodgers, Daniel Moser |
| On-Site Speaker (Planned) |
Aashique Alam Rezwan |
| Abstract Scope |
The mechanical properties and as built geometries of components produced with the laser powder bed fusion (LPBF) process can have significant variation. Predicting mechanical properties and determining their uncertainties is necessary for component qualification. This work presents a probabilistic prediction of microstructure due to the variable laser input in LPBF. The melt pool behavior, i.e., temperature and phase, is first predicted by a high-fidelity thermal fluid model. The microstructure size, shape and crystallographic texture is predicted by using a coupled grain nucleation and kinetic Monte Carlo model for grain growth, considering the variability due to model parameters. This analysis will provide a tool for designers to predict a generalized margin of design for the mechanical properties of additively manufactured parts.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525 |
| Proceedings Inclusion? |
Undecided |
| Keywords |
Additive Manufacturing, Modeling and Simulation, Solidification |