| 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 |
Predicting Temperature Field for Metal Additive Manufacturing using PINN |
| Author(s) |
Bohan Peng, Ajit Panesar |
| On-Site Speaker (Planned) |
Bohan Peng |
| Abstract Scope |
Performing thermomechanical simulation for selective laser melting is a non-trivial and critical task for printability simulation. In addition to the numerical methods, attempts of using a physics-informed neural network (PINN) have shown promise in predicting the temperature fields. In this work, a PINN is constructed with the physics of only homogeneous heat transfer but augmented with data points from a heterogeneous condition with phase change occurring (i.e. from metal power to solid metal). It demonstrates the capability of adopting a PINN (even based on a simple and imperfect physical model) to account for real and more complex phenomena, paving the way for more complex and faster printability simulation for SLM as supplemented by PINN. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |