Measurement of the Uniformity of Thermally Bonded Points in Polypropylene Spunbonded Non-Wovens Using Image Processing and its Relationship With Their Tensile Properties

Abstract

This article aims at the image processing of surface uniformity and thermally bonded points uniformity in polypropylene spunbonded non-wovens. The investigated samples were at two different weights and three levels of non-uniformity. An image processing method based on the k-means clustering algorithm was applied to produce clustered images. The best clustering procedure was selected by using the lowest Davies-Bouldin index. The peak signal-to-noise ratio (PSNR) image quality evaluation method was used to choose the best binary image. Then, the non-woven surface uniformity was calculated using the quadrant method. The uniformity of thermally bonded points was calculated through an image processing method based on morphological operators. The relationships between the numerical outcomes and the empirical results of tensile tests were investigated. The results of image processing and tensile behavior showed that the surface uniformity and the uniformity of thermally bonded points have great impacts on tensile properties at the selected weights and non-uniformity levels. Thus, a sample with a higher level of uniformity and, consequently, more regular bonding points with further bonding percentage depicts the best tensile properties.

Analysis of Basis Weight Uniformity Indexes for the Evaluation of Fiber Injection Molded Nonwoven Preforms

“…A decrease in these two indexes resulted by a lower variance represents a more uniform sample. Emadi et al also adopted Id as index for weight uniformity measurement [16] To overcome the issue with spatial resolution of CV-based methods, new works determined the spatial resolution of images with the Poisson tests. In the study by Pourdeyhimi and Kohel, images are thresholded using a fixed threshold and divided into a different number n of sub-windows and the area fraction in each is calculated [17].…”

“…In pretrials for this work, it has also been found out that the KNN algorithm for the binarization of our images cannot converge in an acceptable time, so the result of the segmentation is not good which will mislead the analysis method. Therefore, the method described by Emadi et al [16] will also not be considered further in this article.…”