Stochastic modeling of discontinuous dynamic recrystallization at finite strains in hcp metals

We present a model that aims to describe the effective, macroscale material response as well as the underlying mesoscale processes during discontinuous dynamic recrystallization under severe plastic deformation. Broadly, the model brings together two well-established but distinct approaches – first, a continuum crystal plasticity and twinning approach to describe complex deformation in the various grains, and second, a discrete Monte-Carlo-Potts approach to describe grain boundary migration and nucleation. The model is implemented within a finite-strain Fast Fourier Transform-based framework that allows for efficient simulations of recrystallization at high spatial resolution, while the grid-based Fourier treatment lends itself naturally to the Monte-Carlo approach. The model is applied to pure magnesium as a representative hexagonal closed packed metal, but is sufficiently general to admit extension to other material systems. Results demonstrate the evolution of the grain architecture in representative volume elements and the associated stress–strain history during the severe simple shear deformation typical of equal channel angular extrusion. We confirm that the recrystallization kinetics converge with increasing grid resolution and that the resulting model captures the experimentally observed transition from single- to multi-peak stress–strain behavior as a function of temperature and rate

Stochastic modeling of discontinuous dynamic recrystallization at finite strains in hcp metals

We present a model that aims to describe the effective, macroscale material response as well as the underlying mesoscale processes during discontinuous dynamic recrystallization under severe plastic deformation. Broadly, the model brings together two well-established but distinct approaches – first, a continuum crystal plasticity and twinning approach to describe complex deformation in the various grains, and second, a discrete Monte-Carlo-Potts approach to describe grain boundary migration and nucleation. The model is implemented within a finite-strain Fast Fourier Transform-based framework that allows for efficient simulations of recrystallization at high spatial resolution, while the grid-based Fourier treatment lends itself naturally to the Monte-Carlo approach. The model is applied to pure magnesium as a representative hexagonal closed packed metal, but is sufficiently general to admit extension to other material systems. Results demonstrate the evolution of the grain architecture in representative volume elements and the associated stress–strain history during the severe simple shear deformation typical of equal channel angular extrusion. We confirm that the recrystallization kinetics converge with increasing grid resolution and that the resulting model captures the experimentally observed transition from single- to multi-peak stress–strain behavior as a function of temperature and rate

High- vs. low-fidelity models for dynamic recrystallization in copper

We investigate the benefits and limitations of mesoscale models for discontinuous dynamic recrystallization (DDRX) in pure copper at elevated temperature with the two-fold aim of capturing microscale mechanisms and predicting the macroscale mechanical response during severe plastic deformation. Differing strongly in their computational expenses and the underlying constitutive assumptions, we introduce and compare an efficient Taylor model (which treats polycrystals as collections of spatially non-interacting grains) with a Field Monte-Carlo Potts (FMCP) model (which resolves spatially inhomogeneous deformation within grains by an FFT-based treatment). Both approaches are based on the same temperature-aware crystal plasticity model for pure copper and introduce only three model parameters for DDRX. The latter are fitted to stress-strain data from uniaxial compression experiments at elevated temperature levels where DDRX is prevalent. Both models capture grain refinement, texture evolution and the stress-strain history with convincing agreement with experiments. The fully-resolved model has highest accuracy, reveals pronounced texture formation, and captures the gradual formation of high-angle grain boundaries within grains as precursors to subgrain formation. The Taylor model, though being significantly more efficient, fails to capture spatially-correlated features including necklace formation and leads to comparatively high prediction errors. However, at temperatures where migration dominates the recrystallization behavior, we observe compelling agreement between the Taylor model and the FMCP model. Last, we demonstrate how reduced-order models facilitate identifying model parameters of the computationally more expensive models

High- vs. low-fidelity models for dynamic recrystallization in copper

We investigate the benefits and limitations of mesoscale models for discontinuous dynamic recrystallization (DDRX) in pure copper at elevated temperature with the two-fold aim of capturing microscale mechanisms and predicting the macroscale mechanical response during severe plastic deformation. Differing strongly in their computational expenses and the underlying constitutive assumptions, we introduce and compare an efficient Taylor model (which treats polycrystals as collections of spatially non-interacting grains) with a Field Monte-Carlo Potts (FMCP) model (which resolves spatially inhomogeneous deformation within grains by an FFT-based treatment). Both approaches are based on the same temperature-aware crystal plasticity model for pure copper and introduce only three model parameters for DDRX. The latter are fitted to stress-strain data from uniaxial compression experiments at elevated temperature levels where DDRX is prevalent. Both models capture grain refinement, texture evolution and the stress-strain history with convincing agreement with experiments. The fully-resolved model has highest accuracy, reveals pronounced texture formation, and captures the gradual formation of high-angle grain boundaries within grains as precursors to subgrain formation. The Taylor model, though being significantly more efficient, fails to capture spatially-correlated features including necklace formation and leads to comparatively high prediction errors. However, at temperatures where migration dominates the recrystallization behavior, we observe compelling agreement between the Taylor model and the FMCP model. Last, we demonstrate how reduced-order models facilitate identifying model parameters of the computationally more expensive models