Knit fabrics are mechanically durable and tough while sufficiently flexible to conform to curved substrates such as the human arm. Recent advancements in CNC knitting enable unprecedented control over the pattern design and functionality of next generation knit fabrics. However, the ability to leverage this granular control to predict and tune the mechanical behavior of these fabrics remains limited due to the complex hierarchical and entangled microstructure. This study establishes a comprehensive experimental and numerical framework to characterize and model the mechanical properties of CNC knitted fabrics. By integrating precision experiments, finite element modeling, and strain energy-based homogenization techniques, we investigate the influence of key knit parameters–stitch length, pattern, and yarn material–on the anisotropic mechanical response of the fabrics. Our findings demonstrate how stiffness, anisotropy, and deformation behavior can be tuned using these parameters in a simple strain-energy model. CNC knitting systems use transition stitches to create fabrics with spatially varying parameters. We demonstrate that material transitions have minimal impact on the fabric’s overall mechanical response, so heterogeneous fabrics can be modeled as patchworks of homogeneous samples. Leveraging our framework, we design and fabricate a tubular knit sleeve with heterogeneous patterns that optimally conforms to a target arm musculature while providing uniform stress distribution. This work bridges the gap between computational modeling and scalable manufacturing, unlocking new possibilities for wearable devices, assistive textiles, and functional applications.
2025-04-09 09:00:00
