Saha Sourav, Kafka Orion L, Lu Ye, Yu Cheng, Liu Wing Kam
Theoretical and Applied Mechanics, Northwestern University, Evanston, Illinois, USA.
National Institute of Standards and Technology (NIST), Materials Measurement Laboratory, Applied Chemicals and Materials Division, Boulder, CO, USA.
Integr Mater Manuf Innov. 2021;10(3). doi: 10.1007/s40192-021-00221-8.
Design of additively manufactured metallic parts requires computational models that can predict the mechanical response of 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能够在考虑微观结构、制造和运行条件的情况下预测零件力学响应的计算模型。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，该挑战要求参与者预测IN625拉伸试样的力学响应随微观结构和制造条件的变化情况。一种代表性体积单元（RVE）方法与在快速傅里叶变换（FFT）力学框架内求解的晶体塑性材料模型相结合，以应对这一挑战。在竞赛过程中，材料模型校准被证明是一项挑战，促使本文引入了一种使用适当广义分解（PGD）的先进材料模型识别方法。最后，展示了一种名为自洽聚类分析（SCA）的机械降阶方法，作为解决这些问题的FFT方法的一种可能替代方案。除了呈现响应分析外，还讨论了与建模相关的一些物理解释和假设。
