Simulation evaluation of knitted mesh structure using morphological operations

Abstract

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.

Yarn-level deformation for weft-knitted stitches

“…And the adjacent masses may be in different layers, so the length of the spring depends on the distances masses in the x-direction and z-direction. The spring length is calculated by equation (10), where l j k j k x , , .…”

“…Textile digital technology provides a feasible solution to this difficulty. 9,10 Based on three-dimensional (3D) computer-aided design (CAD) software, 11,12 cutting-edge simulation technology can be used to achieve virtual and realistic visualization of textile fabrics. Regarding complex knitted structures, it is necessary to develop more targeted simulation techniques and methods.…”

End-to-end fabric defect detection algorithm using lighter feature fusion network

“…Khwakhali et al [13] proposed an algorithm combining GLCM and LBP to extract fabric texture features and classify defects. Li et al [14] using morphological expansion and corrosion algorithms to remove details of non-defect features for irregular mesh fabrics. In summary, traditional fabric defect detection algorithms mainly depend on artificially designed feature extractors, which have weak generalization ability and require manual adjustment of parameters to adapt to the detection scene.…”