|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||A Method for Crystal Plasticity Model Parameter Calibration Based on Bayesian Optimization
||Xiaochuan Sun, Huamiao Wang
|On-Site Speaker (Planned)
Crystallographic plasticity model is an efficient method to bridge the mechanical characteristics of a material at the crystallographic scale to the macroscopic mechanical responses. However, the relatively large number of model parameters makes the calibration cumbersome, especially in systems with hexagonal close packed (HCP) structures like magnesium alloys. This work presents a Bayesian Optimization-based approach and applied it to the calibration of the viscoplastic self-consistent polycrystal plasticity model with twinning and de-twinning scheme (VPSC-TDT) to describe the mechanical behavior of the rare-earth magnesium alloy ZEK100. The result shows that Bayesian Optimization can perform well in such a physical principle-based black-box optimization problem. Combined with a practical tactic, the total trial number can be reduced to around 100, efficiently reducing the time cost. The obtained optimized set of parameters can successfully reproduce the loading path-dependent mechanical behavior of the Mg alloy ZEK100.
||Magnesium, Modeling and Simulation, Machine Learning