Return point memory in knitted fabrics

TL;DR

This paper reveals that knitted fabrics exhibit a form of mechanical memory through hysteresis under cyclic stress, which cannot be explained by traditional models, leading to a new phenomenological understanding.

Contribution

The study uncovers a novel hysteresis behavior in knitted fabrics and extends the Preisach model to explain their memory effects, differing from classical solid-state hysteresis models.

Findings

Knitted fabrics show significant hysteresis and response memory.

Traditional models like viscoelasticity and plasticity do not fully explain the behavior.

A new phenomenological model accurately replicates the observed hysteresis.

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.