Folding-Driven Auxetic Weft Knit Textiles with Integrated Capacitive Sensing

Abstract

Machine knitting provides a scalable platform for manufacturing multifunctional textiles in which geometry, mechanics, and embedded functionality can be programmed at the stitch level. However, predictive design tools capable of linking knit architecture to large-deformation mechanical response remain limited. Here, we develop a reduced-order spring-network model that captures the relaxation, unfolding, and deformation of knitted fabrics composed of checkerboard arrangements of rib and garter patches. The model accurately predicts the corrugated relaxed configuration of the knits and the evolution of local deformations under tensile loading using only linear extensional and torsional springs. Combining simulations with experiments, we show that the programmed unfolding of the corrugations generates tunable auxetic behavior, with both the magnitude of the negative Poisson's ratio and the strain at which it occurs governed by the unit-cell geometry. We further integrate capacitive strain sensing directly during fabrication through partial plating of conductive yarns, eliminating post-processing. The resulting knitted capacitors exhibit programmable tradeoffs between strain sensitivity and sensing range, enabling either highly sensitive sensors over narrow deformation windows or lower-sensitivity sensors capable of measuring larger strains. Together, our modeling framework and fabrication strategy provide a route toward the rational design of mechanically programmable, sensorized knits with tailored shape-morphing and sensing functionalities.