A mesoscale model of microstructure evolution is formulated in the present work by combining a crystal plasticity model with a graph-based vertex algorithm. This provides a versatile formulation capable of capturing finite-strain deformations, development of texture and microstructure evolution through recrystallization. The crystal plasticity model is employed in a finite element setting and allows tracing of stored energy build-up in the polycrystal microstructure and concurrent reorientation of the crystal lattices in the grains. This influences the progression of recrystallization as nucleation occurs at sites with sufficient stored energy and since the grain boundary mobility and energy is allowed to vary with crystallographic misorientation across the boundaries. The proposed graph-based vertex model describes the topological changes to the grain microstructure and keeps track of the grain inter-connectivity. Through homogenization, the macroscopic material response is also obtained. By the proposed modeling approach, grain structure evolution at large deformations as well as texture development are captured. This is in contrast to most other models of recrystallization which are usually limited by assumptions of one or the other of these factors. In simulation examples, the model is in the present study shown to capture the salient features of dynamic recrystallization, including the effects of varying initial grain size and strain rate on the transitions between single-peak and multiple-peak oscillating flow stress behavior. Also the development of recrystallization texture and the influence of different assumptions on orientation of recrystallization nuclei are investigated. Further, recrystallization kinetics are discussed and compared to classical JMAK theory. To promote computational efficiency, the polycrystal plasticity algorithm is parallelized through a GPU implementation that was recently proposed by the authors.

A multiscale modeling framework, combining a graph-based vertex model of microstructure evolution with a GPU-parallelized crystal plasticity model, was recently proposed by the authors. Considering hot rolling of copper, the full capabilities of the model are demonstrated in the present work. The polycrystal plasticity model captures the plastic response and the texture evolution during materials processing while the vertex model provides central features of grain structure evolution through dynamic recrystallization, such as nucleation and growth of individual crystals. The multiscale model makes it possible to obtain information regarding grain size and texture development throughout the workpiece, capturing the effects of recrystallization and heterogeneous microstructure evolution. Recognizing that recrystallization is a highly temperature dependent phenomenon, simulations are performed at different process temperatures. The results show that the proposed modeling framework is capable of simultaneously capturing central aspects of material behavior at both the meso- and macrolevel. Detailed investigation of the evolution of texture, grain size distribution and plastic deformation during the different processing conditions are performed, using the proposed model. The results show a strong texture development, but almost no recrystallization, for the lower of the investigated temperatures, while at higher temperatures an increased recrystallization is shown to weaken the development of a typical rolling texture. The simulations also show the influence of the shear deformation close to the rolling surface on both texture development and recrystallization.

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Crystal plasticity models are often used to model the deformation behavior of polycrystalline materials. One major drawback with such models is that they are computationally very demanding. Adopting the common Taylor assumption requires calculation of the response of several hundreds of individual grains to obtain the stress in a single integration point in the overlying FEM structure. However, a large part of the operations can be executed in parallel to reduce the computation time. One emerging technology for running massively parallel computations without having to rely on the availability of large computer clusters is to port the parallel parts of the calculations to a graphical processing unit (GPU). GPUs are designed to handle vast numbers of floating point operations in parallel. In the present work, different strategies for the numerical implementation of crystal plasticity are investigated as well as a number of approaches to parallelization of the program execution. It is identified that a major concern is the limited amount of memory available on the GPU. However, significant reductions in computational time -up to 100 times speedup -are achieved in the present study, and possible also on a standard desktop computer equipped with a GPU.

Crack-induced hydride formation can occur in specific metallic structures, reducing their mechanical properties and facilitating failure. Grain boundaries are observed to be preferential sites for hydride formation. We present a phasefield approach describing the kinetics of crack-induced hydride formation at a grain boundary, by using the Allen-Cahn formulation and including the increase in grain boundary energy. Hydride development is found to occur at the crack tip and in the grain boundary. These regions seemingly evolve independently, except when the crack is very close to or lies in the grain boundary.