Design of quantification methods for cross-sectional uniformity of functional fiber blended yarns
Source abstract
Abstract
The fiber distribution in yarn structure influences the mechanical, aesthetic, and functional properties of textile products, but methods for characterizing this distribution remain limited. The uniformity of fiber distribution is mostly quantified by the blend irregularity and migration index. Asymmetric and segregated distributions are commonly identified in cross sections of fibers in yarn; however, detecting uniform, wrapped, or core distributions in side-view images is challenging. Therefore, a more effective and versatile method is required for the quantification of cross-sectional uniformity in blended yarns. In this study, two methods for quantifying fiber distribution uniformity were proposed and evaluated. In the window variation method (WVM), which is based on cell counting, windows are randomly selected, and the uniformity of one type of fiber in a yarn cross section calculated according to the variation of the fiber ratio in each window. In the dilation method (DLM), uniformity is quantified according to the dilation area of the targeted fiber. To evaluate these quantification methods, we conducted antibacterial performance tests on 12 chitosan/cotton yarn samples and compared the results with indexes. Comparison of the two methods reveals that the WVM exhibits greater potential because the threshold yarn area ratio and the weights of the corresponding windows can be programmed to achieve certain quantification goals, while the DLM is simple and fast, with fewer variables than the WVM.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Textile materials and evaluations · Industrial Vision Systems and Defect Detection · Dyeing and Modifying Textile Fibers
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Hong Kong Polytechnic University, The Institute of Textiles and Clothing, Hong Kong
Funding
Fund for GRF Project Rated 3.5
References (38)
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