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Abstract

In this research, a new intelligent method was used to evaluate the deformation of stitches in various weft-knitted fabrics based on an ideal shape of stitches and angle of direction of stitches in a knitting machine. To measure deviation of stitch direction against internal stresses, an image analysis technique was applied to images taken from different fabrics with constant front light. In this method, evaluation of fabric regularity with emphasis on the deformation of stitches was studied based on analyzing the images of the fabric using Radon transformation analysis. The index of fabric regularity was obtained from the deviation of stitches from the original direction of ideal regular fabric. Also, the grading of weft-knitted fabric was expanded with a new aspect of regularity grades as a novel grading development. The computer vision method was applied to models of ideal fabric with different stitch sizes. Different weft-knitted fabrics of various structures and yarns were evaluated by the computer vision method. The results showed that this method is capable of grading various weft-knitted fabrics with different fabric structures, densities and yarn types. Therefore, it is possible to use this method for every type of weft-knitted fabric. The results indicated that tuck and miss stitches caused more regularity in fabric, whereas the type of yarn has a major effect on fabric regularity.

Keywords

weft knitted fabric regularity computer vision grading

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New Intelligent Method of Evaluating the Regularity of Weft-knitted Fabrics by Computer Vision and Grading Development

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