Iterative calibration of a shape memory alloy constitutive model from 1D and 2D data using optimization methods

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 identi cation 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 eld data. The optimization framework is verified using 1-D and 3-D nite-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 t very closely to the pseudo-experimental data.