Abdullah Al Amin

Laser Powder Bed Fusion Process Modeling

The Asynchronous Challenge and Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM-Bench) experiments asked modelers to submit predictions for laser absorption, solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process of stationary and 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. Improvement on the past heuristic heat source calibration was proposed relating volumetric energy density (ψ) based on experiments. 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 agreed quantitatively with NIST measurements for different process conditions. A scaling law based on keyhole formation was utilized in predicting the absorptivity of the asynchronous challenge experiment. In addition, an improvement on the heat source model was proposed to relate the VEDσ to melt pool aspect ratio. The model showed further improvement in the prediction of the experimental measurements for melt pool including cases at higher VEDσ. Overall, it was concluded that the appropriate selection of heat source model was crucial in the accurate prediction of melt pool geometry and thermal measurements while bypassing the expensive computational simulations that considers increased physics equations.