Three-dimensionally (3D) knitted technical textiles are spreading into industrial applications, since their geometric, structural and functional performance can be tailored and optimized on fibre-, yarnand fabric levels by customizing yarn materials, knit patterns and geometric shapes. The ability to simulate their complex mechanical behaviour is thus an essential ingredient in the development of a digital workflow for optimal design and manufacture of 3D knitted textiles. Here, we present a multi-scale modelling and simulation framework for the prediction of the nonlinear orthotropic mechanical behaviour of single jersey knitted textiles and its experimental validation. On the meso-scale, representative volume elements (RVEs) of the fabric are modelled as single, interlocked yarn loops and their mechanical deformation behaviour is homogenized using periodic boundary conditions. Yarns are modelled as nonlinear 3D beam elements and numerically discretized using an isogeometric collocation method, where a frictional contact formulation is used to model inter-yarn interactions. On the macro-scale, fabrics are modelled as membrane elements with nonlinear orthotropic material behaviour, which is parameterized by a response surface constitutive model obtained from the meso-scale homogenization. The input parameters of the yarn-level simulation, i.e., mechanical properties of yarns and geometric dimensions of yarn loops in the fabrics, are determined experimentally and subsequent meso-and macro-scale simulation results are evaluated against reference results and mechanical tests of knitted fabric samples. Good agreement between computational predictions and experimental results is achieved for samples with varying stitch values, thus validating our novel computational approach combining efficient meso-scale simulation using 3D beam modelling of yarns with numerical homogenization and nonlinear orthotropic response surface constitutive modelling on the macroscale.

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In this work, we introduce KnitKit , a flexible and customizable system for the computational design and production of functional, multi-material, and three-dimensional knitted textiles. Our system greatly simplifies the knitting of 3D objects with complex, varying patterns that use multiple yarns and stitch patterns by separating the high-level design specification in terms of geometry, stitch patterns, materials or colors from the low-level, machine-specific knitting instruction generation. Starting from a triangular 3D mesh and a 2D texture that specifies knitting patterns on top of the geometry, our system generates the required machine instructions in three major steps. First, the input is processed and the KnitNet data structure is generated. This graph structure serves as an abstract interface between the high-level geometric and knitting configuration and the low-level, machine-specific knitting instructions. Second, a graph rewriting procedure is applied on the KnitNet that produces a sequence of abstract machine actions. Finally, the low-level machine instructions are generated by adapting those abstract actions to a specific machine context. We showcase the potential of this computational approach by designing and fabricating a variety of objects with complex geometries, multiple yarns, and multiple stitch patterns.

CNC knitting technology offers great potential in the creation of thermoformable textiles that can be shaped and stiffened in response to heat. Our research explores how CNC knitting can be used to design and fabricate textiles with precisely allocated material and microstructure layouts. These layouts pre-program specific deformation mechanism(s) into the textile that bias it to form an intended geometry, forgoing the need for a mould during the thermoforming process. We fabricate these smart textiles by knitting two thermal-reactive yarns with different extents of shrinkage, in a double layered structure akin to a bilayer strip. We develop a computational design-to-fabrication pipeline that translates raster images into machine-knittable instructions. Referencing multi-material additive manufacturing principles and self-actuating textiles, this paper proposes several design strategies of dithered gradients, tessellated patches and origami creases, to convert input pixel data into a material distribution layout. When paired with our assisted thermoforming process, this layout induces specific deformations of the textile, such as uni-/multi-axial curling, periodic buckling and sharp folding. Our prototypes implement these strategies on the micro-, meso- and macroscale, leading to the design and fabrication of an architectural cladding panel (700 x 535 x 110 mm) and a patterned clutch bag (200 x 420 x 65 mm).