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.
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KnitGIST: A Programming Synthesis Toolkit for Generating Functional Machine-Knitting Textures
Automatic knitting machines are robust, digital fabrication
devices that enable rapid and reliable production of attractive,
functional objects by combining stitches to produce unique
physical properties. However, no existing design tools support optimization for desirable physical and aesthetic knitted
properties. We present KnitGIST (Generative Instantiation
Synthesis Toolkit for knitting), a program synthesis pipeline
and library for generating hand- and machine-knitting patterns by intuitively mapping objectives to tactics for texture
design. KnitGIST generates a machine-knittable program in
a domain-specific programming language.
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- Award ID(s):
- 1907337
- NSF-PAR ID:
- 10299726
- Date Published:
- Journal Name:
- ACM
- ISSN:
- 0100-6940
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3379337.3415590
