The Digital Measurement And Analytic Study Of Yarn Appearance

The quality of yarn could be determined by appearance parameters during the process of yarn production, it is an important index for the yarn design, production and quality evaluation, mainly including hairiness, diameter, yarn faults, evenness and so on. These parameters are not only directly related to the grade of the terminal textiles production, grade, but also affect the textile production efficiency. Currently, there are several detection methods used for the yarn appearance including photoelectric method, Capacitance method and artificial visual method. The optic-electric and Capacitance methods usually have been proved to be restricted by the objective environment, such as temperature, humidity, chemical composition of the yarn and etc, leading to the limitation of practical application. The subjective method also has some limitations to evaluate the yarn appearance, such as time consuming, low accuracy, influence of subjective factors and et al.A set of dynamic image acquisition and analysis system was proposed in this paper to capture and process the yarn image sequences based on the geometric feature of yarn appearance, including illumination module, drive module, image collection module, which could be used to realize the measurement and characterization of yarn appearance objectively.A series of image processing algorithm for yarn appearance testing and analyzing were design and development utilizing the established image acquiring equipment, including three steps. At the first step, yarn images could be converted into binary images via a series of processing such as gray-scale transformation, background pretreatment, images equalization, images enhancement, dynamical threshold segmentation, yarn hairiness images were extracted through the tilt correction, image filtering, mathematical morphology operation, image segmentation, image thinning and et al. The number of yarn hairs and hairiness index are calculated on the basis of reference line similar to the edge of yarn trunk. The third steps, the yarn trunk images are segmentation by means of the operation of morphological operation, edge smooth and images subtraction. Thereby the unevenness of yarn diameter could be computed in accordance with the corresponding and defined formula.In this paper, image processing results were compared between novel image algorithm and traditional image analysis algorithm, in order to select a optimum method for extracting yarn appearance characterization parameter, improving the robustness of algorithm and achieving the reduction of interference of images noise damage at utmost.Multiple yarn appearance parameters could be calculated with the aid of the analyzing the image sequence simultaneously, using the yarn digital image acquisition system, which could be able to replace the traditional test methods, contributed to the improvement of detection efficiency and accuracy of the image processing algorithms and reduction of testing costs, provide with a extremely important practical significance and bright prospect.