This research presents a preliminary study on finding predictable methods of controlling the self-folding behaviors of weft knit textiles for use in the development of smart textiles and garment devices, such as those with shape memory, auxetic behavior or transformation abilities. In this work, Shima Seiki SDS-One Apex computer-aided knitting technology, Shima Seiki industrial knitting machines, and the study of paper origami tessellation patterns were used as tools to understand and predict the self-folding abilities of weft knit textiles. A wide range of self-folding weft knit structures was produced, and relationships between the angles and ratios of the knit and purl stitch types were determined. Mechanical testing was used as a means to characterize differences produced by stitch patterns, and to further understand the relationships between angles and folding abilities. By defining a formulaic method for predicting the nature of the folds that occur due to stitch architecture patterns, we can better design self-folding fabrics for smart textile applications.

Self-folding behavior is an exciting property of weft-knit fabrics that can be created using just front and back stitches. This behavior is easy to create, but not easy to anticipate and currently cannot be predicted by the existing computer-aided design software that controls industrial knitting machines. This work identifies the edge deformation behaviors that lead to self-folding in weft knits, and methods to characterize the mechanical forces driving these behaviors with regard to chosen manufacturing parameters. With this data and analysis of the fabric deformations, the self-folding behavior was purposely controlled using calculated scaling factors. Furthermore, theoretical equations were developed to mathematically predict these scaling factors, minimizing the trial and error required to design with self-folding behavior and create textiles with novel engineered properties. By understanding the mechanisms responsible for creating these three-dimensional self-folding textiles, they can then be designed in a programmable manner for use in technical applications.

In this paper, we describe algorithms that perform loop order analysis of weft-knitted textiles, which build upon the foundational TopoKnit topological data structure and associated query functions. During knitting, loops of yarn may be overlayed on top of each other and then stitched together with another piece of yarn. Loop order analysis aims to determine the front-to-back ordering of these overlapping loops, given a stitch pattern that defines the knitted fabric. Loop order information is crucial for the simulation of electrical current, water, force, and heat flow within functional fabrics. The new algorithms are based on the assumption that stitch instructions are executed row-by-row and for each row the instructions can be executed in any temporal order. To make our algorithms knitting-machine-independent, loop order analysis utilizes precedence rules that capture the order that stitch commands are executed when a row of yarn loops are being knitted by a two-bed flat weft knitting machine. Basing the algorithms on precedence rules allows them to be modified to adapt to the analysis of fabrics manufactured on a variety of knitting machines that may execute stitch commands in different temporal orders. Additionally, we have developed visualization methods for displaying the loop order information within the context of a TopoKnit yarn topology graph.

Despite their long-time existence and significant capabilities, computational modeling, simulation, and design tools have been underutilized for textiles in general, specifically limiting the ability of knitted textiles to be widely deployed and to reach their full industrial potential in advanced functional fabrics and garments. These computational tools require a robust representation and efficient evaluation of the spatial, material, and physical properties of textile structures. An example of an efficient modeling method for knitted fabrics is TopoKnit, a process-oriented representation for capturing the topology of weft-knitted textiles. In this paper, we extend TopoKnit and present new algorithms that may be used to determine additional topological structures and assess the structural integrity and stability of knitted textiles modeled by this fundamental data structure. We compare our results with outputs from a commercial software system to confirm the effectiveness and validity of our algorithms. These new capabilities provide a foundation for open technologies that can accurately model and predict the geometric and mechanical properties/behaviors of knitted textiles. They will support the development of computational design and analysis tools that will obviate the expensive and wasteful trial-and-error process of knitting and testing actual fabrics in the preproduction phase of textile manufacturing.