Computational Approaches for Plastic Localization Prediction

Plastic Localization in Polycrystalline Aggregates for Computationally Designed  Metallic Materials

The relevant models/simulations for mechanical properties prediction through plastic localization simulation are two classes. The first simulations are based on crystal plasticity (CP). They readily describe the complexity of the interface network (grain boundaries, phases, cell walls, etc.) of metallic materials. However, the resulting fields are continuous gradients that fail to represent physical deformation processes at the nano-scale and, consequently, are inaccurate in predicting mechanical properties involving localization phenomena such as fatigue. The second kind of simulation is based on the physical description of the deformation processes, such as slip or deformation twin. Examples are discrete dislocation dynamics (DDD) or molecular dynamic (MD) simulations. They successfully describe the localization processes. However, they are only representative of small regions of materials and prevent predicting the binding effect of the complex interface networks of polycrystalline materials at the micro- and macro-scale due to restricted computational power. 

The present work intends to fill the gap between small-scale physics-based simulations and large-scale gradient prediction simulations by combining CP-based simulations with an explicit description of deformation processes and quantitative experimental data of plastic localization at the nano-scale over representative measurements. Taking into account the physic of plastic localization and the interface network of material will provide the missing link between small-scale deformation and macroscopic properties. Such a novel approach will guide the design of new microstructures for improved properties, reliability, and affordability of metallic materials.