Performance and prediction of large deformation contractile shape memory alloy knitted actuators

摘要

Compliant, large deformation actuating functional fabrics are essential to next-generation wearables, aerospace structures, and medical devices as they excel at traditional mechanical performance metrics while being soft and pliable. Actuating functional fabrics have been implemented from a variety of multifunctional material systems and fabric manufacturing techniques. A specifically intriguing combination of material system and fabric manufacturing technique are contractile shape memory alloy (SMA) knitted actuators, which generate recoverable actuation deformations of up to 50%, provide actuation forces on the magnitude of 1 N-10 N, are biocompatible, and actuate in response to thermal stimuli. However, the prediction of the knitted functional fabric actuator performance is inherently difficult due to the complex geometry of knitted architectures and the nonlinear material behavior of most multifunctional fibers. This paper presents an empirical model of the contractile SMA knitted actuator performance based on the definition of a dimensionless geometric parameter, the knit index (i(k)). An experimental study of the contractile SMA knitted actuator quasi-static uniaxial thermomechanical performance validates the knit index and the wire diameter as the smallest set of performance-predicting geometric parameters. An empirical model is formulated for the prediction of the contractile SMA knitted actuator performance based on geometric inputs (forward design) and the recommendation of geometric parameters that accomplish specific performance metrics (inverse design). The process of describing and predicting the performance with dimensionless geometric parameters is a step to understanding the mechanics of contractile SMA knitted actuators, has merit for the design of other actuating functional fabrics, and propels the creation of novel wearables, medical devices, and aerospace structures with large actuation deformations.