Search for: All records

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. 

Computational handweaving combines the repeatable precision of digital fabrication with relatively high production demands of the user: a weaver must be physically engaged with the system to enact a pattern, line by line, into a fabric. Rather than approaching co-presence and repetitive labor as a negative aspect of design, we look to current practices in procedural generation (most commonly used in game design and screen-based new media art) to understand how designers can create room for suprise and emergent phenomena within systems of precision and constraint. We developed three designs for blending real-time input with predetermined pattern features. These include: using camera imagery sampled at weaving time; a 1:1 scale tool for composing patterns on the loom; and a live "Twitch'' stream where spectators determine the woven pattern. We discuss how experiential qualities of the systems led to different balances of underdetermination in procedural generation as well as how such an approach might help us think beyond an artifact/experience dichotomy in fabrication. 

Abstract: With recent interest in shape-changing interfaces, material-driven design, wearable technologies, and soft robotics, digital fabrication of soft actuatable material is increasingly in demand. Much of this research focuses on elastomers or non-stretchy air bladders. Computationally-controlled machine knitting offers an alternative fabrication technology which can rapidly produce soft textile objects that have a very different character: breathable, lightweight, and pleasant to the touch. These machines are well established and optimized for the mass production of garments, but compared to other digital fabrication techniques such as CNC machining or 3D printing, they have received much less attention as general purpose fabrication devices. In this work, we explore new ways to employ machine knitting for the creation of actuated soft objects. We describe the basic operation of this type of machine, then show new techniques for knitting tendon-based actuation into objects. We explore a series of design strategies for integrating tendons with shaping and anisotropic texture design. Finally, we investigate different knit material properties, including considerations for motor control and sensing. 

Knitting creates complex, soft fabrics with unique texture properties that can be used to create interactive objects.However, little work addresses the challenges of designing and using knitted textures computationally. We present KnitPick: a pipeline for interpreting hand-knitting texture patterns into KnitGraphs which can be output to machine and hand-knitting instructions. Using KnitPick, we contribute a measured and photographed data set of 472 knitted textures. Based on findings from this data set, we contribute two algorithms for manipulating KnitGraphs. KnitCarving shapes a graph while respecting a texture, and KnitPatching combines graphs with disparate textures while maintaining a consistent shape. KnitPick is the first system to bridge the gap between hand- and machine-knitting when creating complex knitted textures.