| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
A Career in Powder Processing and Additive Manufacturing: A MPMD Symposium Honoring David Bourell
|
| Presentation Title |
Evaluation of Green State Anisotropy in Parts Produced by Binder Jetting, Via Machine Learning Enhanced Discrete Element Modelling |
| Author(s) |
Thomas Benjamin Grippi, Runjian Jiang, Andrii Maximenko, John Kang, Elisa Torresani, Eugene Olevsky |
| On-Site Speaker (Planned) |
Thomas Benjamin Grippi |
| Abstract Scope |
This study examines the anisotropic sintering behavior of stainless steel 316L components produced using the binder jetting process. It investigates the impact of initial particle size distribution and various powder spreading parameters on the heterogeneity of the powder bed through discrete element modeling. Machine learning is employed to process the data and assess potential origins of anisotropy. Starting from a simple bimodal case, the primary goal is to evaluate the impact of parameters on spreading before introducing a realistic powder size distribution into the simulation. Experiments are conducted to evaluate the accuracy of the developed model. Concurrently, a study focusing on binder penetration, using both simulations and experiments, quantifies the binder's role in the anisotropy phenomenon. These experiments will inform the development of the machine learning model and the optimization of spreading parameters in the binder jetting process. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, Powder Materials, Machine Learning |