Coupling Crystal Plasticity Research Articles

Multi-Scale Modeling of the Impact Response of a Strain-Rate Sensitive High-Manganese Austenitic Steel

A multi-scale modeling approach was applied to predict the impact response of a strain rate sensitive high-manganese austenitic steel. The roles of texture, geometry and strain rate sensitivity were successfully taken into account all at once by coupling crystal plasticity and finite element (FE) analysis. Specifically, crystal plasticity was utilized to obtain the multi-axial flow rule at different strain rates based on the experimental deformation response under uniaxial tensile loading. The equivalent stress – equivalent strain response was then incorporated into the FE model for the sake of a more representative hardening rule under impact loading. The current results demonstrate that reliable predictions can be obtained by proper coupling of crystal plasticity and FE analysis even if the experimental flow rule of the material is acquired under uniaxial loading and at moderate strain rates that are significantly slower than those attained during impact loading. Furthermore, the current findings also demonstrate the need for an experiment-based multi-scale modeling approach for the sake of reliable predictions of the impact response.

Coupled crystal plasticity – Probabilistic cellular automata approach to model dynamic recrystallization in magnesium alloys

This study presents a framework to simulate dynamic recrystallization (DRX) in hexagonal closed packed (HCP) metals and alloys using crystal plasticity based finite element model (CPFEM) coupled with a probabilistic cellular automata (CA) approach, as applied to Mg alloys. The CPFEM takes as input the microstructural information from experimental measurements and computes local dislocation density evolution corresponding to active deformation modes. DRX proceeds via nucleation of new grains and their subsequent growth. A new nucleation criterion based on local mismatch in dislocation density is implemented in the model. Nucleation sites are defined solely from the local inhomogeneouty of dislocation density within a grain or across grain boundaries. Cellular automata model with probabilistic state switching rule predicts the growth of viable nucleation sites with high misorientation angle depending on the difference in the stored energy of the nucleus and the stored energy of the surrounding matrix. State switching probability rule is based on the velocity of the grain boundary between the nucleus and the matrix grains. The new approach is validated with recrystallization data on AZ31 sheets. The model captures both the microscopic (texture) and the macroscopic (stress–strain response) properties during DRX.