Moleinia Zara, Bahr David F
School of Materials Engineering, Purdue University, West Lafayette, IN 47907, USA.
Materials (Basel). 2021 Jan 18;14(2):450. doi: 10.3390/ma14020450.
The current work centers on multi-scale approaches to simulate and predict metallic nano-layers' thermomechanical responses in crystal plasticity large deformation finite element platforms. The study is divided into two major scales: nano- and homogenized levels where Cu/Nb nano-layers are designated as case studies. At the nano-scale, a size-dependent constitutive model based on entropic kinetics is developed. A deep-learning adaptive boosting technique named single layer calibration is established to acquire associated constitutive parameters through a single process applicable to a broad range of setups entirely different from those of the calibration. The model is validated through experimental data with solid agreement followed by the behavioral predictions of multiple cases regarding size, loading pattern, layer type, and geometrical combination effects for which the performances are discussed. At the homogenized scale, founded on statistical analyses of microcanonical ensembles, a homogenized crystal plasticity-based constitutive model is developed with the aim of expediting while retaining the accuracy of computational processes. Accordingly, effective constitutive functionals are realized where the associated constants are obtained via metaheuristic genetic algorithms. The model is favorably verified with nano-scale data while accelerating the computational processes by several orders of magnitude. Ultimately, a temperature-dependent homogenized constitutive model is developed where the effective constitutive functionals along with the associated constants are determined. The model is validated by experimental data with which multiple demonstrations of temperature effects are assessed and analyzed.
当前的工作集中在多尺度方法上，以在晶体塑性大变形有限元平台中模拟和预测金属纳米层的热机械响应。该研究分为两个主要尺度：纳米尺度和均匀化尺度，其中以铜/铌纳米层作为案例研究。在纳米尺度上，开发了一种基于熵动力学的尺寸相关本构模型。建立了一种名为单层校准的深度学习自适应增强技术，通过一个适用于与校准完全不同的广泛设置的单一过程来获取相关本构参数。该模型通过实验数据进行验证，结果吻合良好，随后对多个关于尺寸、加载模式、层类型和几何组合效应的案例进行行为预测，并对其性能进行了讨论。在均匀化尺度上，基于微正则系综的统计分析，开发了一种基于均匀化晶体塑性的本构模型，目的是在保持计算过程准确性的同时加快计算速度。据此，实现了有效的本构泛函，其中相关常数通过元启发式遗传算法获得。该模型通过纳米尺度数据得到了良好验证，同时将计算过程加速了几个数量级。最终，开发了一种与温度相关的均匀化本构模型，其中确定了有效的本构泛函以及相关常数。该模型通过实验数据进行验证，并对温度效应的多个实例进行了评估和分析。
