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).

中文翻译：

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集成计算材料工程，预测Inconel 625选择性激光熔化的熔池尺寸和3D晶粒结构

这项工作将美国国家标准与技术研究院（NIST）增材制造（AM）基准测试系列（AM Bench）问题AMB2018-02中六个挑战中的四个挑战的仿真结果与实验数据进行了比较。这种比较类似于测试案例，用于评估AM预测功能的技术成熟度（TML），可将其用于改进行业中的AM产品。这些解决方案用于预测熔池几何形状，冷却速率，凝固晶粒形状及其3D结构。这些结果是使用增材制造参数预测器（AMP ²）软件获得的。放大器²是由Applied Optimization，Inc.（AO）开发的集成计算材料工程（ICME）软件套件。使用熔池物理的热计算流体动力学（CFD）解决方案获得熔池几何形状。通过热CFD模型预测熔池几何形状，平均轨迹横截面，热梯度的3D分布以及液固界面速度，并将其用作凝固晶粒结构计算的输入。使用元胞自动机（CA）对晶粒形状和3D结构进行建模。AO因在Inconel 625（IN625）合金裸板上的三个单激光轨迹中的晶粒结构预测而获得第二名。