Physics guided heat source for quantitative prediction of IN718 laser additive manufacturing processes

This paper addresses Challenge 3 of the 2022 NIST additive manufacturing benchmark, predicting solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for IN718 laser powder bed fusion. An in-house AM-CFD code with a cylindrical heat source is calibrated using a HOPGD-based surrogate model and a keyhole formation scaling law. The calibrated model quantitatively agrees with NIST measurements for various process conditions, and a further improved physics-guided heat source model shows enhanced predictions, particularly at higher volumetric energy densities. The study highlights the importance of appropriate heat source parameterization for accurate predictions while reducing computational cost.

Abdullah Al Amin, Yangfan Li, Ye Lu, Xiaoyu Xie, Zhengtao Gan, Satyajit Mojumder, Gregory J. Wagner