No abstract

A computing approach based on the warp-knitted fabric structure was presented in this paper to visualize Textronic laces by taking a fall-plate lapping in one course as a targeted unit. Its geometric description was firstly presented on both the projection plane and normal plane of structure depth. Based on this and an improved Blinn–Phong reflection model, illuminative interaction along the yarn width and lapping length was separately studied and then overlaid with empirical weight coefficients to fit a function to solve fall-plate lapping facial illumination. Because the displayed facial feature was illustrated by distinguishing pixels on screen, the continuous solved function was discretized into average grids and each grid was integrating to obtain segmental appearance variation. The variation was then mapped from a Cartesian coordinate to a screen coordinate of pixels to be displayed, while the hidden lapping was eliminated according to the depth-buffer algorithm.

High-quality simulation of fabric structural features is significant for realizing the morphological prediction of nonuniform mesh structures and building numerical simulation of physical properties. For lace textiles which have hundreds and thousands of variable meshes, an objective and unified standard to evaluate structural deformation is quite challenging. It needs to comprehensively and quantitatively conclude a result, instead of visual and subjective judgment. Therefore, this paper proposed an image-based method to mathematically solve accuracy by comparing morphological features from both simulation result and real fabric photograph. Jacquardtronic lace textiles were fabricated as experimental samples. Based on morphological expansion and corrosion algorithms, non-characteristic lapping details were eroded from binary images with only featured single-pixel contours of irregular meshes. Shape descriptor of each featured image was represented by a moment vector of seven Hu invariant moments. Then the morphological vectors of both simulated and real fabric images were substituted into a defined equation of similarity measurement. This image-based evaluation model effectively avoids defects of subjective visual observation and geometric measurement methods.

As a new kind of functional fiber, hollow coffee carbon polyester (HCCP) has the advantages of antibacterial and warmth retention properties. HCCP’s wear-ability performances in knitted textiles were studied by changing the specifications of the raw materials, blending ratios and knitted structures. Plain knit, interlock knit and Ponte knit fabrics were prepared with compact siro-spun HCCP/cotton yarns in five different blended ratios. Eight groups of experiments were conducted to test the performances of the mechanical properties, comfortable capability and antibacterial behavior. Then a comprehensive wear-ability evaluation was made based on the grey clustering analysis method. Results revealed that fabrics with a higher blended ratio of HCCP showed more preferred performance in terms of bursting strength, air permeability, moisture absorption, warmth preservation and antibacterial property, but the anti-pilling performance showed a downward trend. Compared to plain stitch, the interlock knit and Ponte knit fabrics showed a visibly better result.