Determining the Structural Parameters That Affect Overall Properties of Warp Knitted Fabrics Using Cluster Analysis

Abstract

Two-bar warp knitted structures are grouped by cluster analysis of mechanical and physical properties, and the resulting classified groups show distinctive characteristics. The first group represents highly smooth, full and soft properties compared with other groups, and the characteristics of the second group are less smooth, full and soft. The fabrics of the third group are very stiff and smooth, and those of the last group are the least stiff, smooth, full, and soft. By analyzing the structures within and between groups, the structural parameters affecting the overall properties of warp knitted fabrics are deter mined. The results show that the formalized factors affecting the characteristics of warp knitted fabrics are the run-in ratio, pillar-lapping movement, direction of mutual guide bar movement with parallel or counter lapping, and closed or open lap type.

Woven filter media used in wet filtration processes: investigation of pore size distribution, air and water permeability and finding correlations between them

“…As Yoon and Park 28 have mentioned, cloths classified in the cluster analysis and gathered in a group had a higher degree of association within the same group and dissimilar properties between the groups. According to Figure 3, three clusters could be distinguished for the studied samples: one for cloths 2 and 3, another for cloths 1, 8 and 9, and the last for cloths 4, 5 and 7.…”

Data mining and machine learning in textile industry

“…In a hierarchical agglomerative clustering algorithm, there are three different types of intercluster distance measurement: single link, complete link, and average link. While some textile studies 60,67 used only one approach (i.e., single link) to determine the effect of textile structural parameters on the performance, some of them 61,68 used and compared all of the approaches to evaluate the relationship between the textile parameters and the properties. Li et al 67 combined both hierarchical and nonhierarchical algorithms with the aim to classify fabrics without noticeable color shade differences.…”

“…For example, prediction of fabric elongation, 25 drying times of bobbins, 4 fabric pilling, 39 the air permeability of fabrics. 15 • Construction of models to reduce the consumption of textile-related materials, such as fabrics, yarns, dyes, and sewing threads 5 • Discovering patterns that can be used to produce better textile endproducts 13,61,68 • Analyzing textile data to achieve better customer satisfaction…”

Knitted Preforms for Composite Applications

“…Warp knitting is by far the most versatile fabric production system in textiles providing design variations to produce elastic or stable, open or closed structure, flat, tubular or three-dimensional (3-D) structure with a maximum width of over 6 m [16]. Based on the physical and mechanical properties, two guide bar warp knitted structures were categorized into four groups by cluster analysis so that the structure-property correlation can be successfully adopted for designing warp knit preforms to meet desired qualifications [17]. In warp knitted preforms, each increase in the extent of underlap tends to make the structure stronger widthwise whereas pillar or chain stitches are incorporated in the structure to improve the lengthwise strength of the knitted preform [16].…”