| 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)
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| Presentation Title |
An Ensemble Kalman Filter Method for Laser Powder Bed Fusion Temperature Estimation, Augmented with Adaptive Meshing and Joint Estimation of the Absorptivity |
| Author(s) |
Nathaniel Wood, Edwin Schwalbach, Sean Donegan, Andrew Gillman, David J. Hoelzle |
| On-Site Speaker (Planned) |
Nathaniel Wood |
| Abstract Scope |
Methods for in-situ process monitoring of Laser Powder Bed Fusion (PBF) typically use large quantities of training data, since accurate predictive models are too computationally expensive. The Ensemble Kalman Filter (EnKF) overcomes this limitation by using the available measurements to apply self-tuned corrections to naïve model predictions. Here, we estimate the PBF temperature field, and the naïve model is Finite Element Method (FEM) heat conduction. The laser absorptivity is a modeling hyperparameter. We test three implementations of the EnKF using data from previous experiments: Implementation 1 uses a time-varying FEM mesh that is only dense nearby the laser (adaptive meshing), with fixed absorptivity. Implementation 2 uses a time-invariant mesh while jointly estimating temperature and absorptivity. Finally, Implementation 3 combines joint estimation and adaptive meshing. Implementations 1 and 2 show good accuracy in the heat affected zone, which enables accurate identification of several defect types, but performance suffers in Implementation 3. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |