| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
A Data-driven Surrogate Model for Time-dependent Scanwise Thermal Simulations of Laser Powder Bed Fusion Parts |
| Author(s) |
Berkay Bostan, Shawn Hinnebusch, David Anderson, Albert To |
| On-Site Speaker (Planned) |
Berkay Bostan |
| Abstract Scope |
The intricate thermal conditions that arise throughout the laser powder bed fusion (LPBF) process depend on the local geometry and have significant impact on the part quality in terms of defect formation, microstructure, and residual distortion. However, due to the large length scale difference between the laser beam (µm) and part (cm), scanwise simulations are intractable for part-scale models due to the immense computational expense. In this study, a surrogate model which represents time-dependent, part-scale scanwise thermal simulations is proposed. The developed architecture consists of a sequence of deep neural networks (DNNs), and long short-term memory (LSTM) units. The input vector encompasses information related to geometric, thermal conditions, scanning strategy, and simulation parameters. A 100x speedup is achieved by the proposed model, thus enabling defect formation and microstructure simulations of centimeters scale parts. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |