Computationally Efficient Prediction of Molten Pool Size in Multi-track Laser Melting via Surrogate Modeling

This work presents an original surrogate model for the accurate and computationally efficient prediction of molten pool size in multi-track laser melting over a large domain at operating conditions relevant to laser powder bed fusion. While high-fidelity models can accurately predict the molten pool dynamics, the high computational expense limits their application to a few short tracks on small domains. Conduction models, on the other hand, are orders of magnitude cheaper to evaluate but lack the necessary physics for accurate predictions. This research presents a surrogate model that combines the computational efficiency of the conduction model with the accuracy of the high-fidelity model. A conduction model and high-fidelity model are simulated over a small scan pattern to generate training data of the highly transient molten pool depth and width. A data-driven model, consisting of a fuzzy basis function network, is trained with the aforementioned data. The conduction model is then simulated over a larger scan pattern, the results are input into the trained surrogate model, thereby outputting high-fidelity predictions of the molten pool size over a larger scan pattern. Comparison with experimental results shows this surrogate modeling framework provides reasonably accurate predictions of the molten pool depth and width (average error of -5.2% and 4.6%, respectively) and is a valid way to extend computationally intensive high-fidelity models to larger and more industrially relevant scan patterns.