Numerical material testing using finite element polycrystalline model based on successive integration method
Numerical material testing using finite element polycrystalline model based on successive integration method
复制标题
基于逐次积分法的有限元多晶模型数值材料测试
DOI：
10.1016/j.promfg.2018.07.207
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
S. Onoshima and T. Oya
中科院分区：
文献类型：
--
作者：
T. Oya;J. Yanagimoto;K. Ito;G. Uemura and N. Mori;S. Onoshima and T. Oya
近年来，金属成形技术的快速发展给高强度先进金属塑性变形的准确预测带来了难题。如果应用的是表现出强各向异性的金属，则建议使用某些高级材料模型。通常，先进的材料模型需要通过实验来确定大量的材料参数。为了避免这种情况，如果这种方法可以作为一些难以进行的实验的替代方法，那么构建数值材料测试可能是有益的。为此，我们开发了基于逐次积分法的有限元多晶模型材料数值试验方法。该方法由基于晶体塑性的模型和用于捕捉多晶金属微观结构行为的深度神经网络组成。在这项研究中，描述了所提出的基于材料学习概念的方法，并用实验数据进行了验证。在学习阶段，将面内拉伸试验获得的实验数据作为教学数据，经过多尺度材料学习后，虚拟材料将获得对未学习的面外力学特性的共性。在这项工作中，对等双轴应力应变关系的预测与文献中的实验数据吻合较好。
Recent rapid progress in metal forming has brought difficult problems when it comes to accurate prediction in plastic deformation in which high-strength advanced metals are used. If a metal that exhibits strong anisotropy is applied, the use of some advanced material model is recommended. Usually, advanced material models require large number of material parameters to be determined by experiments. To avoid this situation, construction of numerical material testing could be beneficial if such methodology can be an alternative for some difficult-to-conduct experiments. Therefore, we have developed a numerical material testing using finite element polycrystalline model based on successive integration method. The proposed method consists of crystal plasticity-based model and a deep neural network to capture the microstructural behavior of polycrystalline metals. In this study, a description of the proposed method that is based on the concept of material learning, and some verification with experimental data are presented. In the learning phase, experimental data obtained from in-plane tensile tests are provided as teaching data, and after the multiscale material learning, the virtual material will acquire generality to non-learned out-of-plane mechanical characteristics. In this work, prediction for equi-biaxial stress-strain relation resulted in acceptable agreement with experimental data from a literature.
影响因子：
3.3
作者：
A. Brahme;M. Winning;D. Raabe
通讯作者：
A. Brahme;M. Winning;D. Raabe
DOI：
10.1016/0001-6160(66)90196-9
发表时间：
1966
期刊：
影响因子：
--
作者：
W. Hosford
通讯作者：
W. Hosford
DOI：
--
发表时间：
2004
期刊：
影响因子：
--
作者：
D. Green;K. Neale;S. Macewen;A. Makinde;R. Perrin
通讯作者：
R. Perrin