Iterative Calibration of a Shape Memory Alloy Constitutive Model from 1-D and 2-D Data Using Optimization Methods

摘要

Shape memory alloy constitutive models have been shown to accurately predict 1-D and 3-D material response under general thermomechanical loading. As with any constitutive model, however, the degree to which simulation results match experimental data is dependent on the accurate calibration of model parameters. This work presents a general framework for the identification of SMA material parameters using numerical optimization methods and experimental results that include both 1-D data (i.e., stress-strain and strain-temperature line plots) as well as 2-D digital image correlation (DIC) strain field data. The optimization framework is verified using 1-D and 3-D finite-element-based simulated results as pseudo-experimental data. The study shows that the proposed optimization methods can identify SMA parameters in an automated fashion using data taken from multiple types of experiment, identifying parameters that fit very closely to the pseudo-experimental data.