Amin, Abdullah AlLi, YangfanLu, YeXie, XiaoyuGan, ZhengtaoMojumder, SatyajitWagner, Gregory J.Liu, Wing Kam2024-03-062024-03-062024-02-19Amin, Abdullah Al, Yangfan Li, Ye Lu, Xiaoyu Xie, Zhengtao Gan, Satyajit Mojumder, Gregory J. Wagner, and Wing Kam Liu. “Physics Guided Heat Source for Quantitative Prediction of IN718 Laser Additive Manufacturing Processes.” Npj Computational Materials 10, no. 1 (February 19, 2024): 1–14. https://doi.org/10.1038/s41524-024-01198-6.https://doi.org/10.1038/s41524-024-01198-6http://hdl.handle.net/11603/31839Challenge 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.14 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Creative Commons Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/Computational methodsEngineeringText