Challenge 4 of the Air Force Research Laboratory additive manufacturing modeling challenge series asks the participants to predict the grain-average elastic strain tensors of a few specific challenge grains during tensile loading, based on experimental data and extensive characterization of an IN625 test specimen. In this article, we present our strategy and computational methods for tackling this problem. During the competition stage, a characterized microstructural image from the experiment was directly used to predict the mechanical responses of certain challenge grains with a genetic algorithm-based material model identification method. Later, in the post-competition stage, a proper generalized decomposition (PGD)-based reduced order method is introduced for improved material model calibration. This data-driven reduced order method is efficient and can be used to identify complex material model parameters in the broad field of mechanics and materials science. The results in terms of absolute error have been reported for the original prediction and re-calibrated material model. The predictions show that the overall method is capable of handling large-scale computational problems for local response identification. The re-calibrated results and speed-up show promise for using PGD for material model calibration.

空军研究实验室增材制造建模挑战系列的挑战 4 要求参与者预测一些特定挑战晶粒的晶粒平均弹性应变张量在拉伸载荷期间，基于实验数据和 IN625 试样的广泛表征。在本文中，我们介绍了解决此问题的策略和计算方法。在比赛阶段，通过基于遗传算法的材料模型识别方法，直接使用来自实验的特征显微结构图像来预测某些挑战颗粒的机械响应。随后，在赛后阶段，引入了一种基于适当广义分解（PGD）的降阶方法来改进材料模型校准。这种数据驱动的降阶方法非常有效，可用于识别力学和材料科学广泛领域的复杂材料模型参数。已针对原始预测和重新校准的材料模型报告了绝对误差的结果。预测表明，整体方法能够处理局部响应识别的大规模计算问题。重新校准的结果和加速显示了使用 PGD 进行材料模型校准的希望。