Numerical analysis and modeling of shape memory alloy with stochastic structural variations

Abstract Shape memory alloy (SMA) textiles provide a promising soft actuator strategy for handling fragile or shaped objects without causing damage, showing great potential in wearable and medical devices. The spatial randomness in the geometric structure of SMA-based textiles, induced by mechanical forces during manufacturing, introduces displacement deviations, which challenge the accurate prediction of mechanical behaviors. This study develops an innovative stochastic modeling framework for high-fidelity simulations, facilitating the targeted design and optimization of SMA textile actuators. The framework incorporates: (1) quantitative analysis of randomness in material weaving structures using deviation-based Pearson correlation, combined with PCA for dimensionality reduction, (2) node-based SMA structural modeling utilizing series expansion methods, and (3) validation with experimental data using correlation length and RMSD demonstrates model accuracy, with a precision of 94.8% and 98.1%, respectively. This approach lays the foundation for the future optimization of high-performance textile-based soft actuators.