History and temperature dependent cyclic crystal plasticity model with material-invariant parameters
History and temperature dependent cyclic crystal plasticity model with material-invariant parameters
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DOI：
10.1016/j.ijplas.2022.103494
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发表时间：
2023-02
影响因子：
9.8
通讯作者：
Farhan Ashraf;G. Castelluccio
中科院分区：
文献类型：
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作者：
Farhan Ashraf;G. Castelluccio
金属材料的循环变形取决于不同长度尺度上多种机制的相互作用。固溶体原子、空位、晶界和森林位错干扰位错滑移并增加宏观强度。在循环载荷下的单相金属材料中，固着亚结构中位错密度的局部化解释了应变硬化的重要部分。循环时，这些位错结构演变跨越稳定的配置，这取决于应变accumulation.This工作的子结构敏感的晶体塑性模型能够量化的循环硬化历史在不同温度下的单相FCC材料。该框架预测的位错亚结构的循环演化的基础上的激活的交叉滑移激活Al，Cu，Ni单晶和多晶高达0.5同源温度。随着温度和变形的增加，交叉滑移引起位错结构的转变，这预示着没有任何额外的规定的二次硬化。此外，该方法依赖于特定于位错子结构而不是材料系统的材料不变的介观参数。因此，我们表明，晶体塑性预测能力可以增强参数化模型与单晶实验数据从多个材料具有共同的子结构。因此，晶体塑性模型在材料之间共享参数信息，而不需要额外的单晶实验数据进行校准。
Cyclic deformation of metallic materials depends on the interaction of multiple mechanisms across different length scales. Solid solution atoms, vacancies, grain boundaries, and forest dislocations interfere with dislocation glide and increase the macroscopic strength. In single phase metallic materials under cyclic loading, the localization of dislocation densities in sessile substructures explains a significant fraction of the strain hardening. Upon cycling, these dislocation structures evolve across stable configurations, which depend on the strain accumulation.This work advances substructure-sensitive crystal plasticity models capable of quantifying the cyclic hardening history at various temperatures for single phase FCC materials. The framework predicts the cyclic evolution of dislocation substructure based on the activation of cross slip activation for Al, Cu, and Ni single- and poly-crystals up to 0.5 homologous temperature. The increase in cross slip with temperature and deformation induces a transformation in dislocation structures, which predicts secondary hardening without any additional provision. Moreover, the approach relies on material-invariant mesoscale parameters that are specific to dislocation substructures rather than a material system. Hence, we demonstrate that crystal plasticity predictive power can be augmented by parameterizing the model with single crystal experimental data from multiple materials with common substructures. As a result, the crystal plasticity model shares parameter information across materials without the need for additional single crystal experimental data for calibration.