Texture-Based Optimization of Crystal Plasticity Parameters: Application to Zinc and Its Alloy

Abstract

Evolutionary algorithms have become an extensively used tool for identification of crystal plasticity parameters of hexagonal close packed metals and alloys. However, the fitness functions were usually built using the experimentally measured stress–strain curves. Here, the fitness function is built by means of numerical comparison of the simulated and experimental textures. Namely, the normalized texture difference index is minimized. The evolutionary algorithm with the newly developed fitness function is tested by performing crystal plasticity parameter optimization for both pure zinc and zinc-magnesium alloy. These materials are promising candidates for bioabsorbable implants due to good biocompatibility and optimal corrosion rate. Although their mechanical properties in the as-cast state do not fulfill the requirements, they can be increased by means of hydrostatic extrusion. The developed modeling approach enabled acquisition of the crystal plasticity parameters and analysis of the active deformation mechanisms in zinc and zinc-magnesium alloy subjected to hydrostatic extrusion. It was shown that although slip systems are the main deformation carrier, compressive twinning plays an important role in texture evolution. However, the texture is also partially affected by dynamic recrystallization which is not considered within the developed framework.