Data-driven Investigation of Cotton Fabric Behavior Modified by Straight and Zig-Zag Stitches

TL;DR

This study employs a data-driven approach using constitutive models to analyze how straight and zig-zag stitches alter the mechanical behavior of cotton fabric, providing detailed insights for textile design and smart wearable development.

Contribution

It introduces a novel data-driven methodology utilizing the three-network model to quantify intermolecular parameters affected by different stitches on cotton fabric.

Findings

Stitches significantly alter fabric mechanics with 99.99% confidence.

Distributed strain energy analysis reveals failure mechanisms.

Data-driven parameters enhance understanding of fabric behavior.

Abstract

In this article, we demonstrate a data-driven approach to investigate the behavior of cotton fabric modified by straight and zig-zag stitches. Existing literature in understanding the mechanical behavior of soft materials (e.g., textile-based fibers or fabrics) heavily relies on stress-strain analyses. However, the strain-induced deformation behavior can be further analyzed by taking advantage of data-driven constitutive models. Such an approach reveals intermolecular parameters that can be utilized further in design and development analyses. For that, we exhibit the altered mechanics of base cotton fabric induced by two types of singular stitches (straight and zig-zag). We have sewn simple straight and zig-zag cotton stitches to investigate the mechanics of the base cotton fabrics using uniaxial stress-strain experimental data. Then, we leveraged the constitutive models (i.e., three-network model, TNM) obtained from MCalibration software to reveal eleven intermolecular parameters for data-driven investigations. Our experimental analyses, combined with the data, suggest a 99.99% confidence in assessing the mechanical impact of stitches on cotton fabrics. We have also used distributed strain energy to analyze the mechanics and failure of the base and stitched fabrics. Once adopted, our study may contribute to an improved understanding of the production of smart wearables and e-textiles.