Nonlinear Poisson's Ratio for Modeling Hyperelastic Capacitive Sensors

Abstract

Abstract Highly stretchable capacitive sensors are of great interest for soft robotic control due to their ability to measure relatively large strains. These sensors are often multilayered materials, with one or more of the layers made from silicones filled with functional particles. However, the models used to describe the material behavior do not always account for the hyperelastic nature of the silicones, the altered material properties due to fillers, and potential anisotropy due to the layered structure. Large errors arise when predicting capacitance using widespread assumptions of linear elastic mechanics and isotropic material properties. This study demonstrates how these modeling assumptions are inadequate for predicting sensor performance, and compares alternative models based on empirical material mechanics. The Poisson's ratio of multi‐layered hyperelastic capacitors is measured in both the width and thickness directions by imaging the sensor dimensions during strain. The results indicate that the sensors are anisotropic and have a strain‐dependent Poisson's ratio, demonstrating the validity of the proposed model. Considering these properties in capacitance models will lead to an improved ability to predict sensor performance, especially at high strains.