Tools to predict and understand the self-folding behavior of knitted textiles

Chelsea Elizabeth Knittel

doi:10.17918/00000083

This dissertation aims to develop a fundamental understanding of the self-folding behaviors of weft knit fabrics. Weft knit textile machinery is a manufacturing technology that has existed since the sixteenth century but is now being repositioned as a twenty-first century advanced manufacturing tool. Using only basic stitches, the knit stitch and the purl stitch, weft knitting provides a unique structure-based behavior, self-folding, which allows for the creation of complex dimensional textiles in a single manufacturing step, without the need for secondary processes to add dimensionality. These folded textiles are similar to folded origami and kirigami structures that have been studied for a variety of engineering applications, including architecture and space exploration devices. The self-assembly behavior of the weft knit structure could be used to further expand the potential of these designs, using automated production processes in wide variety of materials. To date, how self-folding occurs in weft knit textiles is not well understood. Methods for modeling these behaviors do not currently exist, preventing their use in advanced manufacturing design. Unlike the systems developed for common engineering materials, design of these structures currently relies on trial and error, through development of numerous prototypes. While self-folding can be easily created, controlling the type of folding that occurs is difficult to understand and predict. While similar in appearance to origami and kirigami, their folding principles are not directly applicable to knitted textiles. Furthermore, there is a lack of systems for modeling complex structures such as those with self-folding behavior, since the commercially available modeling tools for weft knits were developed mainly for the garment industry rather than for the production of engineered materials. This work set out to understand how self-folding behavior occurs, and how it is affected by machine processing parameters and changes in yarn material. In this dissertation, a top down approach was used to develop a fundamental understanding of self-folding in weft knits, and to create tools to allow for improved predictive design of engineered textiles. The developed tools include a system of mapping the folding directions of knit and purl stitch patterns, a method of scaling stitch patterns to alter their folding behavior, and equations to predict the scaling factor. The combined use of these tools will enable designers and engineers to more efficiently predict and control the outcome of the fabrics they produce. Additionally, this work lead to the development of a novel topological modeling platform for the weft knit structure, furthering the knowledge of how the geometries and topologies of the loop structure create self-folding, and deepening the understanding of the physics driving this behavior. This dissertation uses fundamental knowledge of self-folding behavior in weft knit structures to create an engineered system of design that will facilitate the use of textiles in novel applications requiring advanced manufacturing tools to predict properties prior to fabrication. Keywords: Advanced manufacturing, Engineered textiles, Knit and purl, Knitting, Modeling, Self-folding

Tools to predict and understand the self-folding behavior of knitted textiles

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Tools to predict and understand the self-folding behavior of knitted textiles

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