| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Modeling, Simulation and Machine Learning
|
| Presentation Title |
Efficient Laser Powder Bed Fusion Microstructure and Texture Modeling |
| Author(s) |
Gregory D. Wong, Gregory S Rohrer, Anthony Rollett |
| On-Site Speaker (Planned) |
Gregory D. Wong |
| Abstract Scope |
Laser powder bed fusion additive manufacturing (LPBF) is a significant technology in modern metal manufacturing. However, given its recent emergence, there is a lack of clear understanding of the correlation between printing parameters (laser power, laser velocity, hatch spacing, hatch rotation, etc.) and the microstructure and texture of the parts. This talk will cover methodologies to predict 3D as-built microstructures including grain orientation from the printing parameters with emphasis on efficiency. First, a high computational cost (days per cubic mm) version of the SPPARKS LPBF package modified to include textured solidification will be shown. Following this, the intermediate cost (hours per cubic mm) “Pass Scale” model will be shown which makes multiple assumptions about melt pool shape and solidification improving computational cost. Finally, a machine learning model trained on the previous two model’s outputs will be shown as the most efficient means of texture-inclusive microstructure prediction. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, Modeling and Simulation, Machine Learning |