The evolution of the crystallographic orientation field in a polycrystalline sample of copper is mapped in three dimensions as tensile strain is applied. Using forward-modeling analysis of high-energy X-ray diffraction microscopy data collected at the Advanced Photon Source, the ability to track intragranular orientation variations is demonstrated on an similar to 2 mu m length scale with similar to 0.1 degrees orientation precision. Lattice rotations within grains are tracked between states with similar to 1 degrees precision. Detailed analysis is presented for a sample cross section before and after similar to 6\% strain. The voxel-based (0.625 mu m triangular mesh) reconstructed structure is used to calculate kernel-averaged misorientation maps, which exhibit complex patterns. Simulated scattering from the reconstructed orientation field is shown to reproduce complex scattering patterns generated by the defected microstructure. Spatial variation of a goodness-of-fit or confidence metric associated with the optimized orientation field indicates regions of relatively high or low orientational disorder. An alignment procedure is used to match sample cross sections in the different strain states. The data and analysis methods point toward the ability to perform detailed comparisons between polycrystal plasticity computational model predictions and experimental observations of macroscopic volumes of material.

We examine the relationship between local gradients in orientation, which are quantified with the Kernel Average Misorientation, and the grain boundary network in an interstitial-free steel sheet, before and after 12\% tensile strain. A portion of the unstrained microstructure is used as input to a full-field spectral viscoplastic code that simulates the same deformation. The orientation gradients are concentrated near grain boundaries in both experiments and simulation. Mapping out stress gradients in the simulation suggests that the development of orientation gradients is strongly correlated with such gradients.

Various degrees of rolling reductions account for diverse recrystallization mechanisms and thus different microstructural and texture features. The development of deformation and recrystallization textures is discussed based on experimental data and results of finite element and crystal plasticity simulations. A recrystallization model is presented that incorporates the microstructural heterogeneities and changes in local stored energy. The experimental observations and results of crystal plasticity calculations testify that orientation selection during recrystallization is controlled by low stored energy nucleation which is incorporated in the recrystallization model. Results of texture simulations show that the evolution of \100\<130> and \011\<233> components is related to a particle stimulated nucleation mechanism.

During anisotropic curvature driven grain growth, high-energy grain boundaries are preferentially eliminated, thus leading to interface texture development and a higher population of low energy grain boundaries. However, when stress is introduced as an additional driving force, the dynamics of grain growth change. To model these effects, a three dimensional anisotropic multi- level set model was modified in order to account for the effect of stress field on grain growth. For this mesoscale study, grain boundaries were treated as dislocation structures and their associated net Burgers vectors were calculated using the misorientation information and boundary inclinations. Using these net Burgers vectors and their associated densities, additional forces due to stress field were calculated via the Peach-Koehler equation. Qualitative comparisons of 5 parameter grain boundary character distribution will be carried out in order to analyze the differences in texture evolution during grain growth.

A calibrated Monte Carlo (cMC) approach, which quantifies grain boundary kinetics within a generic setting, is presented. The influence of misorientation is captured by adding a scaling coefficient in the spin flipping probability equation, while the contribution of different driving forces is weighted using a partition function. The calibration process relies on the established parametric links between Monte Carlo (MC) and sharp-interface models. The cMC algorithm quantifies microstructural evolution under complex thermomechanical environments and remedies some of the difficulties associated with conventional MC models. After validation, the cMC approach is applied to quantify the texture development of polycrystalline materials with influences of misorientation and inhomogeneous bulk energy across grain boundaries. The results are in good agreement with theory and experiments.