Integrated Computational Materials Engineering to Predict Melt-Pool Dimensions and 3D Grain Structures for Selective Laser Melting of Inconel 625

Abstract

This work presents a comparison of simulation results with the experimental data for four of the six challenges within the National Institute of Standards and Technology (NIST) Additive Manufacturing (AM) Benchmark Test Series (AM Bench) problem AMB2018-02. This comparison is akin to a test case to assess the technology maturity level (TML) for the AM predictive capabilities that can be utilized to improve AM products in the industry. The solutions are for the prediction of melt-pool geometry, cooling rate, solidification grain shapes, and their 3D structure. These results were obtained using the Additive Manufacturing Parameter Predictor (AMP2) software. AMP2 is an Integrated Computational Materials Engineering (ICME) suite of software developed by Applied Optimization, Inc. (AO). The melt-pool geometry is obtained using a thermal-computational fluid dynamics (CFD) solution of melt-pool physics. The melt-pool geometry, mean track cross section, 3D distribution of thermal gradient, and the liquid-to-solid interface velocity are predicted by the thermal-CFD model and utilized as input for the solidification grain structure computation. The grain shapes and 3D structure are modeled using cellular automata (CA). AO received a second-place award for predicting the grain structure within three single laser tracks on a bare plate of alloy Inconel 625 (IN625).