Prediction of fabric curling mechanical behavior based on spatial pentagonal network model and classify-planar theory

Abstract

Owing to its excellent elasticity and adaptability, weft-knitted fabric finds widespread application in various industries, including clothing, construction, and industrial packaging. The curling behavior that sometimes occurs in fabrics can have a certain impact on processes such as fabric leveling, printing, and dyeing, as well as composite material substrate production. To achieve prediction of curling behavior, the paper proposes a modeling method based on a spatial pentagonal network, as well as a prediction approach for curling based on classify-planar theory and energy method, taking weft-knitted fabric as an example. First, the approach uses the structural characteristic points and structural parameters of the yarn unit to establish the vertices of the spatial pentagonal network, and then inserts spline curves and sweeps it to achieve the modeling of fabric yarn unit. Second, the mechanical theory of fabric curling in XY plane and Z direction is proposed to establish a classify-planar simulation idea for curling prediction, and then the formulas for the length and height of curling of macroscopic fabrics are derived using the energy method. Finally, the experimental measurement shows that the curling length and height errors are 5.735% and 6.105%, respectively. Therefore, this method contributes to the advancement of intelligent fabric manufacturing and offers guidance for the research and application in clothing design, fabric printing and dyeing, fabric smoothing, and composite material substrate selection.