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Dynamic Measurement of Foam-Sized Yarn Properties from Yarn Sequence Images

机译:从纱线序列图像动态测量泡沫尺寸纱线的性能

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摘要

Unlike the normal sizing method, the foam sizing had been proven to be a low-add-on technology. To investigate the effect of foam sizing, film thickness, sized-yarn evenness, and size penetration rate were necessary to evaluate the performances of foam-sized yarns. However, the conventional image analysis of sized-yarn cross sections primarily relied on artificial testing with a low efficiency. This paper proposed a novel dynamic method to measure the sized-yarn properties including film thickness, sized-yarn evenness, and size penetration rate based on yarn sequence images captured from a moving yarn. A method of dynamic threshold module was adopted to obtain threshold for segmenting yarns in the sequence images. K-means clustering algorithm was applied to segment pixels of the images into yarn and background. To further remove burrs and noise in the images, two judgment templates were carried out to extract the information of yarn core. The film thickness, sized-yarn evenness, and size penetration rate were measured based on the yarn core of each frame in sequence images. In order to compare with the experimental results of the dynamic method, the yarn properties of the same samples were tested by static and artificial testing. Results revealed that the proposed method could efficiently and accurately detect the film thickness, sized-yarn evenness, and size penetration rate.
机译:与常规上浆方法不同,泡沫上浆已被证明是一种低附加技术。为了研究泡沫上浆的效果,必须评估膜厚,上浆纱均匀度和上浆渗透率,以评估泡沫上浆纱的性能。但是,常规的对尺寸纱线横截面的图像分析主要依靠人工测试,效率低下。本文提出了一种新的动态方法,它基于从运动的纱线上捕获的纱线序列图像来测量包括膜厚,浆纱均匀度和浆纱穿透率在内的浆纱特性。采用动态阈值模块的方法获得序列图像中分割纱线的阈值。应用K均值聚类算法将图像的像素分割为纱线和背景。为了进一步消除图像中的毛刺和噪声,采用了两个判断模板来提取纱线芯的信息。基于序列图像中各帧的纱芯,测定膜厚,上浆纱均匀度和上浆速度。为了与动态方法的实验结果进行比较,通过静态和人工测试对相同样品的纱线性能进行了测试。结果表明,该方法可以有效,准确地检测出膜的厚度,上浆纱的均匀度和上浆率。

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