| About this Abstract |
| Meeting |
2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Symposium
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2024 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2024)
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| Presentation Title |
Enhancing Predictive Accuracy in Thermal Modeling of Refractory Alloys: A Bayesian Approach Integrating Analytical Models and Experimental Data |
| Author(s) |
Brent G. Vela, Peter Morcos, Cafer Acemi, Alaa Elwany, Ibrahim Karaman, Raymundo Arróyave |
| On-Site Speaker (Planned) |
Brent G. Vela |
| Abstract Scope |
We aim to identify processing parameters to reduce porosity in additive manufacturing (AM) of refractory alloys. Printability maps based on melt pool dimensions are effective in this regard but are data-hungry. We propose enhancing printability predictions from the analytical Eagar-Tsai (ET) with experimental data using Bayesian methods. To create a thermal model that is both fast-acting and accurate we propose correcting analytical models with experimental data in a Bayesian manner. Specifically, we propose modifying both 1) the prior mean function 2) and the co-variance function of Gaussian Process Regressors (GPR, a Bayesian non-parametric regressor that is defined by a prior mean and co-variance) with the physics captured in the ET model. This essentially creates a data-corrected ET model. Adhering to best practices, we benchmark the effect of the prior mean and the physics-constrained co-variance function using a 2-fold cross validation scheme, demonstrating our method is effective under data-sparse conditions. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |