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.

An Elastic-Plastic Analysis of Polycrystalline Structure Using Crystal Plasticity Modelling – Theory and Benchmark Tests

“…The plastic velocity gradient is calculated as a sum of slip rates on all slip systems and this way links the macro-and micro-scales (Eq. 14) [25,33].…”

Crystal Plasticity Parameter Optimization in Cyclically Deformed Electrodeposited Copper – A Machine Learning Approach

“…Besides taking into account these microstructural details, the hardening equations of crystal plasticity are typically similar to conventional plasticity and typically account for isotropic and kinematic hardening. Various optimization algorithms such as gradient optimization [3], Newton-Raphson algorithm [4], Levenberg-Marquardt method [5], Bayesian optimization [6], particle swarm optimization [7] and evolutionary algorithms [8][9][10][11][12][13][14][15][16][17][18][19] are used in order to establish the correct set of material parameters (see the introduction in [19] for a thorough discussion). However, all of them share the basic disadvantage: one has to repeat the optimization in the case of having new material or new test result.…”

Microstructure evolution of Zn–0.2Mg–0.8Mn(wt-%) alloys with different initial textures during room-temperature compression

“…More recently, Zn–Mg [5,6], Zn–Mg–Mn [19], Zn–Li–Mn [13] and Zn–Li–Cu [14] alloys could meet the strength and elongation requirements. The microstructure and mechanical properties of Zn–Mg had been comprehensively studied [5,6,20]. The combination of Mg alloying and deformation contributed to grain refinement and texture component and furtherly improved both the strength and plasticity.…”