Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of the parts considering the microstructural, manufacturing, and operating conditions. This article documents our response to Air Force Research Laboratory (AFRL) Additive Manufacturing Modeling Challenge 3, which asks the participants to predict the mechanical response of tensile coupons of IN625 as function of microstructure and manufacturing conditions. A representative volume element (RVE) approach was coupled with a crystal plasticity material model, solved within the fast Fourier transformation (FFT) framework for mechanics, to address the challenge. During the competition, material model calibration proved to be a challenge, prompting the introduction in this manuscript of an advanced material model identification method using proper generalized decomposition (PGD). Finally, a mechanistic reduced order method called self-consistent clustering analysis (SCA) is shown as a possible alternative to the FFT method for solving these problems. Apart from presenting the response analysis, some physical interpretation and assumptions associated with the modeling are discussed.

增材制造金属零件的设计需要计算模型，该模型可以在考虑微观结构、制造和操作条件的情况下预测零件的机械响应。本文记录了我们对空军研究实验室 (AFRL) 增材制造建模挑战 3 的回应，该挑战要求参与者根据微观结构和制造条件预测 IN625 拉伸试样的机械响应。代表性体积元素 (RVE) 方法与晶体塑性材料模型相结合，在用于力学的快速傅里叶变换 (FFT) 框架内解决，以应对挑战。在比赛过程中，材料模型校准被证明是一个挑战，促使在本手稿中引入一种使用适当的广义分解 (PGD) 的高级材料模型识别方法。最后，一种称为自洽聚类分析 (SCA) 的机械降阶方法被证明是解决这些问题的 FFT 方法的可能替代方法。除了提供响应分析外，还讨论了与建模相关的一些物理解释和假设。