IDENTIFYING MATERIAL PARAMETERS FOR A MICRO-POLAR PLASTICITY MODEL VIA X-RAY MICRO-COMPUTED TOMOGRAPHIC (CT) IMAGES: LESSONS LEARNED FROM THE CURVE-FITTING EXERCISES
IDENTIFYING MATERIAL PARAMETERS FOR A MICRO-POLAR PLASTICITY MODEL VIA X-RAY MICRO-COMPUTED TOMOGRAPHIC (CT) IMAGES: LESSONS LEARNED FROM THE CURVE-FITTING EXERCISES
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DOI：
10.1615/intjmultcompeng.2016016841
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发表时间：
2016
影响因子：
1.4
通讯作者：
Kun Wang;WaiChing Sun;S. Salager;S. Na;Ghonwa Khaddour
中科院分区：
文献类型：
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作者：
Kun Wang;WaiChing Sun;S. Salager;S. Na;Ghonwa Khaddour
与传统的一阶连续介质模型不同，传统的一阶连续介质模型的材料参数可以通过在呈现均匀变形的物质点进行反问题来识别，而高阶连续介质模型需要来自变形梯度导数的信息。这项研究涉及一种综合的实验-数值方法，旨在识别高阶连续介质模型的材料参数。利用显微CT图像和宏观应力-应变曲线的组合作为数据库，构造了一个新的有限元逆问题，该问题识别出同时匹配宏观本构响应和细观微极运动学的材料参数的最佳值。我们的结果表明，约束最优化方法预测的最优特征长度对用于定义反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所产生的破坏模式(局部破坏模式与扩散破坏模式)以及耦合的应力响应。这一结果表明，仅使用平均颗粒直径来校准特征长度可能不足以产生可靠的正演预测。
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.