skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Engineering Multifunctional Spacer Fabrics Through Machine Knitting
Machine knitting is an increasingly accessible fabrication technology for producing custom soft goods. However, recent machine knitting research has focused on knit shaping, or on adapting hand-knitting patterns. We explore a capability unique to machine knitting: producing multilayer spacer fabrics. These fabrics consist of two face layers connected by a monofilament filler yarn which gives the structure stiffness and volume. We show how to vary knit patterning and yarn parameters in spacer fabrics to produce tactile materials with embedded functionality for forming soft actuated mechanisms and sensors with tunable density, stiffness, material bias, and bristle properties. These soft mechanisms can be rapidly produced on a computationally-controlled v-bed knitting machine and integrated directly into soft objects.  more » « less
Award ID(s):
1718651
PAR ID:
10227085
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21)
Page Range / eLocation ID:
1 to 12
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Soft robots adapt passively to complex environments due to their inherent compliance, allowing them to interact safely with fragile or irregular objects and traverse uneven terrain. The vast tunability and ubiquity of textiles has enabled new soft robotic capabilities, especially in the field of wearable robots, but existing textile processing techniques (e.g., cut‐and‐sew, thermal bonding) are limited in terms of rapid, additive, accessible, and waste‐free manufacturing. While 3D knitting has the potential to address these limitations, an incomplete understanding of the impact of structure and material on knit‐scale mechanical properties and macro‐scale device performance has precluded the widespread adoption of knitted robots. In this work, the roles of knit structure and yarn material properties on textile mechanics spanning three regimes–unfolding, geometric rearrangement, and yarn stretching–are elucidated and shown to be tailorable across unique knit architectures and yarn materials. Based on this understanding, 3D knit soft actuators for extension, contraction, and bending are constructed. Combining these actuation primitives enables the monolithic fabrication of entire soft grippers and robots in a single‐step additive manufacturing procedure suitable for a variety of applications. This approach represents a first step in seamlessly “printing” conformal, low‐cost, customizable textile‐based soft robots on‐demand. 
    more » « less
  2. Digital knitting machines have the capability to reliably manufacture seamless, textured, and multi-material garments, but these capabilities are obscured by limiting CAD tools. Recent innovations in computational knitting build on emerging programming infrastructure that gives full access to the machine’s capabilities but requires an extensive understanding of machine operations and execution. In this paper, we contribute a critical missing piece of the knitting-machine programming pipeline–a program optimizer. Program optimization allows programmers to focus on developing novel algorithms that produce desired fabrics while deferring concerns of efficient machine operations to the optimizer. We present KODA, the Knit-program Optimization by Dependency Analysis method. KODA re-orders and reduces machine instructions to reduce knitting time, increase knitting reliability, and manage boilerplate operations that adjust the machine state. The result is a system that enables programmers to write readable and intuitive knitting algorithms while producing efficient and verified programs 
    more » « less
  3. Abstract Knitting turns yarn, a 1D material, into a 2D fabric that is flexible, durable, and can be patterned to adopt a wide range of 3D geometries. Like other mechanical metamaterials, the elasticity of knitted fabrics is an emergent property of the local stitch topology and pattern that cannot solely be attributed to the yarn itself. Thus, knitting can be viewed as an additive manufacturing technique that allows for stitch-by-stitch programming of elastic properties and has applications in many fields ranging from soft robotics and wearable electronics to engineered tissue and architected materials. However, predicting these mechanical properties based on the stitch type remains elusive. Here we untangle the relationship between changes in stitch topology and emergent elasticity in several types of knitted fabrics. We combine experiment and simulation to construct a constitutive model for the nonlinear bulk response of these fabrics. This model serves as a basis for composite fabrics with bespoke mechanical properties, which crucially do not depend on the constituent yarn. 
    more » « less
  4. We show that a linear model is sufficient to accurately estimate the quantity of yarn that goes into a knitted item produced on an automated knitting machine. Knitted fabrics are complex structures, yet their diverse properties arise from the arrangement of a small number of discrete, additive operations. One can estimate the masses of each of these basic yarn additions using linear regression and, in turn, use these masses to estimate the overall quantity (and local distribution) of yarn within any knitted fabric. Our proposed linear model achieves low error on a range of fabrics and generalizes to different yarns and stitch sizes. This paves the way for applications where having a known yarn distribution is important for accuracy (e.g., simulation) or cost estimation (e.g., design). 
    more » « less
  5. Knitting interloops one-dimensional yarns into three-dimensional fabrics that exhibit behaviour beyond their constitutive materials. How extensibility and anisotropy emerge from the hierarchical organization of yarns into knitted fabrics has long been unresolved. We seek to unravel the mechanical roles of tensile mechanics, assembly and dynamics arising from the yarn level on fabric nonlinearity by developing a yarn-based dynamical model. This physically validated model captures the mechanical response of knitted fabrics, analogous to flexible metamaterials and biological fibre networks due to geometric nonlinearity within such hierarchical systems. Fabric anisotropy originates from observed yarn–yarn rearrangements during alignment dynamics and is topology-dependent. This yarn-based model also provides a design space of knitted fabrics to embed functionalities by varying geometric configuration and material property in instructed procedures compatible to machine manufacturing. Our hierarchical approach to build up a knitted fabric computationally modernizes an ancient craft and represents a first step towards mechanical programmability of knitted fabrics in wide engineering applications. 
    more » « less