| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Symposium
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2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Experimental Validations of an Ensemble Kalman Filter Method for Powder Bed Fusion Temperature Estimation |
| Author(s) |
Nathaniel Wood, Edwin Schwalbach, Sean Donegan, Andrew Gillman, David J. Hoelzle |
| On-Site Speaker (Planned) |
Nathaniel Wood |
| Abstract Scope |
Standard methods for estimating Laser Powder Bed Fusion (PBF) process variables rely on costly and time-consuming training data. The Ensemble Kalman Filter (EnKF) avoids this burden by using in-situ measurements to apply self-tuned corrections to physics-based model predictions of the process. Here, the process variable is the PBF temperature field, and the model is a Finite Element Method (FEM) description of heat conduction. In this work, we describe implementing the EnKF with this model and two PBF measurement architectures. Using data from previous experiments, we demonstrate EnKF effectiveness under three subsets of PBF process physics, which tests how it well it corrects increasing modeling error: solely heat conduction, melting a metal surface, and fusing layers of powder. The EnKF accurately estimates heat affected zone temperatures in every test, which is critical for PBF quality control, but incorrect estimates at isolated FEM nodes become more frequent as modeling error increases. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |