Texture evolution and inhomogeneous deformation of polycrystalline Cu during uniaxial compression are investigated at the grain scale by combining crystal plasticity finite element method(CPFEM) with particle swarm op...Texture evolution and inhomogeneous deformation of polycrystalline Cu during uniaxial compression are investigated at the grain scale by combining crystal plasticity finite element method(CPFEM) with particle swarm optimization(PSO) algorithm. The texture-based representative volume element(TBRVE) is used in the crystal plasticity finite element model, where a given number of crystallographic orientations are obtained by means of discretizing the orientation distribution function(ODF) based on electron backscattered diffraction(EBSD) experiment data. Three-dimensional grains with different morphologies are generated on the basis of Voronoi tessellation. The PSO algorithm plays a significant role in identifying the material parameters and saving computational time. The macroscopic stress–strain curve is predicted based on CPFEM, where the simulation results are in good agreement with the experimental ones. Therefore, CPFEM is a powerful candidate for capturing the texture evolution and clarifying the inhomogeneous plastic deformation of polycrystalline Cu. The simulation results indicate that the <110> fiber texture is generated finally with the progression of plastic deformation. The inhomogeneous distribution of rotation angles lays the foundation for the inhomogeneous deformation of polycrystalline Cu in terms of grain scale.展开更多
为提升哈里斯鹰优化算法收敛精度,解决易陷入局部最优等问题,提出了一种基于迭代混沌精英反向学习和黄金正弦策略的哈里斯鹰优化算法(gold sine HHO,GSHHO)。利用无限迭代混沌映射初始化种群,运用精英反向学习策略筛选优质种群,提高种...为提升哈里斯鹰优化算法收敛精度,解决易陷入局部最优等问题,提出了一种基于迭代混沌精英反向学习和黄金正弦策略的哈里斯鹰优化算法(gold sine HHO,GSHHO)。利用无限迭代混沌映射初始化种群,运用精英反向学习策略筛选优质种群,提高种群质量,增强算法的全局搜索能力;使用一种收敛因子调整策略重新计算猎物能量,平衡算法的全局探索和局部开发能力;在哈里斯鹰的开发阶段引入黄金正弦策略,替换原有的位置更新方法,提升算法的局部开发能力;在9个测试函数和不同规模的栅格地图上评估GSHHO的有效性。实验结果表明:GSHHO在不同测试函数中具有较好的寻优精度和稳定性能,在2次机器人路径规划中路径长度较原始HHO算法分别减少4.4%、3.17%,稳定性分别提升52.98%、63.12%。展开更多
基金Projects(51305091,51475101) supported by the National Natural Science Foundation of ChinaProject(20132304120025) supported by Specialized Research Fund for the Doctoral Program of Higher Education,China
文摘Texture evolution and inhomogeneous deformation of polycrystalline Cu during uniaxial compression are investigated at the grain scale by combining crystal plasticity finite element method(CPFEM) with particle swarm optimization(PSO) algorithm. The texture-based representative volume element(TBRVE) is used in the crystal plasticity finite element model, where a given number of crystallographic orientations are obtained by means of discretizing the orientation distribution function(ODF) based on electron backscattered diffraction(EBSD) experiment data. Three-dimensional grains with different morphologies are generated on the basis of Voronoi tessellation. The PSO algorithm plays a significant role in identifying the material parameters and saving computational time. The macroscopic stress–strain curve is predicted based on CPFEM, where the simulation results are in good agreement with the experimental ones. Therefore, CPFEM is a powerful candidate for capturing the texture evolution and clarifying the inhomogeneous plastic deformation of polycrystalline Cu. The simulation results indicate that the <110> fiber texture is generated finally with the progression of plastic deformation. The inhomogeneous distribution of rotation angles lays the foundation for the inhomogeneous deformation of polycrystalline Cu in terms of grain scale.
文摘为提升哈里斯鹰优化算法收敛精度,解决易陷入局部最优等问题,提出了一种基于迭代混沌精英反向学习和黄金正弦策略的哈里斯鹰优化算法(gold sine HHO,GSHHO)。利用无限迭代混沌映射初始化种群,运用精英反向学习策略筛选优质种群,提高种群质量,增强算法的全局搜索能力;使用一种收敛因子调整策略重新计算猎物能量,平衡算法的全局探索和局部开发能力;在哈里斯鹰的开发阶段引入黄金正弦策略,替换原有的位置更新方法,提升算法的局部开发能力;在9个测试函数和不同规模的栅格地图上评估GSHHO的有效性。实验结果表明:GSHHO在不同测试函数中具有较好的寻优精度和稳定性能,在2次机器人路径规划中路径长度较原始HHO算法分别减少4.4%、3.17%,稳定性分别提升52.98%、63.12%。
