| 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 Physics-guided Data-driven Model for Enhanced Temperature Prediction and Control of LPBF Additive Manufacturing |
| Author(s) |
Cheng-Hao Chou, Chinedum Okwudire |
| On-Site Speaker (Planned) |
Cheng-Hao Chou |
| Abstract Scope |
A hybrid (i.e., physics-guided data-driven) model is proposed for temperature prediction and control of laser powder bed fusion (LPBF) additive manufacturing. Parts produced by the LPBF process are subject to deformation or other defects due to the thermal behavior during the manufacturing process, which can be resolved by model-based control techniques. However, existing temperature prediction models are either inaccurate or computationally costly, and hence are not suitable for closed-loop control. To overcome these deficiencies, the authors propose a linear hybrid model, which cascades a physics-based finite difference method (FDM) model with a linear data-driven model that aims to correct the temperature prediction of the FDM model by learning the unmodeled dynamics. By simulation, the proposed hybrid model is shown to achieve significant improvement in the temperature prediction accuracy compared to the baseline model (i.e., the physics-based FDM model). |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |