Return point memory in knitted fabrics
Elizabeth J. Dresselhaus, Sonja Hellebrand, Rajyasri Roy, Kranthi K. Mandadapu, Sanjay Govindjee
TL;DR
This work reveals that knitted fabrics exhibit robust return point memory under uniaxial cyclic loading, where the stress response remembers the maximum prior strain $\varepsilon_{\mathrm{max}}$ and forms nested, congruent hysteresis loops that wipe out when a higher maximum is reached. The authors develop an extended Preisach model in which yarn-contact hysterons are modulated by an entanglement strain $\varepsilon_t$ and $\varepsilon_{\mathrm{max}}$, decomposing $\varepsilon = \varepsilon_e + \varepsilon_t$ with $\sigma = E(\varepsilon_{\mathrm{max}})\,\varepsilon_e$, and enforcing evolution via a rebound inequality and KKT conditions. The model reproduces nonlocal memory, wiping-out, and congruency observed in experiments and provides a simple, tunable framework for memory in loop-based fabrics. These findings have implications for designing memory-enabled textiles for soft robotics, sensors, and morphing devices, and motivate exploration of memory across diverse fabric architectures by controlling topology, tension, and material composition.
Abstract
The tunable mechanical response of knitted fabrics underpins applications ranging from soft robotics and artificial muscles to morphing electromagnetic field sensors. Elasticity in fabrics emerges from the bending of yarn in the knitted structure; however, properties beyond elasticity are relatively unexplored. Here, we demonstrate that knitted fabrics subjected to cyclic uniaxial stress exhibit significant hysteresis and the remarkable ability to "remember" their response to previous deformations -- reminiscent of classical return point memory in magnetic systems. The hysteretic behavior deviates from the two standard models of hysteresis that usually apply to solid-state materials, viscoelasticity and plasticity. Thus, we develop a phenomenological extension of the Preisach model of hysteresis which well replicates our data, and discuss implications of these results on the underlying mechanisms of memory in knitted fabrics.
