Search

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 is implemented to accurately predict these experiments. Heuristic heat source calibration is proposed relating volumetric energy density (ψ) based on experiments available in the literature. The parameters of the heat source of the computational model are initially calibrated based on a Higher Order Proper Generalized Decomposition- (HOPGD) based surrogate model. The prediction using the calibrated heat source agrees quantitatively with NIST measurements for different process conditions (laser spot diameter, laser power, and scan speed). A scaling law based on keyhole formation is 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 is proposed to relate the Volumetric Energy Density (VEDσ) to the melt pool aspect ratio. The model shows further improvement in the prediction of the experimental measurements for the melt pool, including cases at higher VEDσ. Overall, it is concluded that the appropriate selection of laser heat source parameterization scheme along with the heat source model is crucial in the accurate prediction of melt pool geometry and thermal measurements while bypassing the expensive computational simulations that consider increased physics equations. [ABSTRACT FROM AUTHOR]

Creation of new alloys through in-situ alloying in additive manufacturing is being actively explored to improve the properties and performance of manufactured parts. Multiphysics models are often used to investigate the underlying physics controlling AM processes using high-fidelity simulations of individual powder particle melting within the meltpool. In this paper, we present a novel multiphysics-multiphase model for simulating multicomponent powder beds during the AM process. A convection–diffusion formulation for component mass fraction, in conjunction with the Navier–Stokes equations and enthalpy form of the energy equation, enable us to predict the phase evolution in multicomponent beds and the dispersion of solute metals in the melt pool during the process. A non-equilibrium phase diagram (NEPD) model that incorporates the associated phase diagram is introduced to obtain the mass fraction in both solid and liquid phases as well as local temperature and liquid fraction by efficiently solving a set of nonlinear equations locally at each computational cell. This work introduces a numerical method for multicomponent Al-Zr binary material system for light-weight aluminum alloys, specifically demonstrating its efficacy and accuracy for eutectic solidification of this multicomponent. [ABSTRACT FROM AUTHOR]