Physics Guided Heat Source for Quantitative Prediction of the Laser Track Measurements of IN718 in 2022 NIST AM Benchmark Test

Abstract Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM-Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process using moving lasers. An in-house developed Additive Manufacturing Computational Fluid Dynamics code (AM-CFD) combined with a cylindrical heat source was implemented to accurately predict these experiments. Heuristic heat source calibration was proposed relating volumetric energy density (ψ) based on experiments available in the literature. The parameters of the heat source of the computational model were initially calibrated based on a Higher Order Proper Generalized Decomposition- (HOPGD) based surrogate model. The prediction using the calibrated heat source agreed quantitatively with NIST measurements for different process conditions. A scaling law based on keyhole formation was also utilized in calibrating the parameters of the cylindrical heat source and predicting the challenge experiments. In addition, an improvement on the heat source model was proposed to relate the Volumetric Energy Density (VEDσ) to the melt pool aspect ratio. The model showed further improvement in the prediction of the experimental measurements for the melt pool including cases at higher VEDσ. Overall, it was concluded that the appropriate selection of parameterization scheme along with the heat source model was crucial in the accurate prediction of melt pool geometry and thermal measurements while bypassing the expensive computational simulations that consider increased physics equations.