Unlike a conventional first-order continuum model, the material parameters of which can be identified via an inverse problem conducted at material point that exhibits homogeneous deformation, a higher-order continuum model requires information from the derivative of the deformation gradient. This study concerns an integrated experimental-numerical procedure designed to identify material parameters for higher-order continuum models. Using a combination of microCT images and macroscopic stress–strain curves as the database, we construct a new finite element inverse problem which identifies the optimal value of material parameters that matches both the macroscopic constitutive responses and the meso-scale micropolar kinematics. Our results indicate that the optimal characteristic length predicted by the constrained optimization procedure is highly sensitive to the types and weights of constraints used to define the objective function of the inverse problems. This sensitivity may in return affect the resultant failure modes (localized vs. diffuse), and the coupled stress responses. This result signals that using the mean grain diameter alone to calibrate the characteristic length may not be sufficient to yield reliable forward predictions.

中文翻译：

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通过 X 射线显微计算机断层扫描 (CT) 图像识别微极性塑性模型的材料参数：从曲线拟合练习中学到的经验教训

与传统的一阶连续模型不同，其材料参数可以通过在呈现均匀变形的材料点处进行的逆问题来识别，而高阶连续模型需要来自变形梯度导数的信息。这项研究涉及一个集成的实验数值程序，旨在确定高阶连续模型的材料参数。使用微CT图像和宏观应力应变曲线的组合作为数据库，我们构建了一个新的有限元反问题，该问题确定了与宏观本构响应和中尺度微极运动学相匹配的材料参数的最佳值。我们的结果表明，约束优化程序预测的最佳特征长度对用于定义逆问题目标函数的约束类型和权重高度敏感。这种敏感性可能反过来影响最终的失效模式（局部与扩散）和耦合应力响应。该结果表明，单独使用平均晶粒直径校准特征长度可能不足以产生可靠的前向预测。