Abstract
Textile yarns are the fundamental building blocks in the fabric industry. The measurement of the diameter of the yarn textile and fibers is crucial in textile engineering as the diameter size and distribution can affect the yarn’s properties, and image processing can provide automatic techniques for faster and more accurate determination of the diameters. In this paper, facile and new methods to measure the yarn’s diameter and its individual fibers diameter based on image processing algorithms that can be applied to microscopic digital images. Image preprocessing such as binarization and morphological operations on the yarn image were used to measure the diameter automatically and accurately compared to the manual measuring using ImageJ software. In addition to the image preprocessing, the circular Hough transform was used to measure the diameter of the individual fibers in a yarn’s cross-section and count the number of fibers. The algorithms were built and deployed in a MATLAB (R2020b, The MathWorks, Inc., Natick, Massachusetts, United States) environment. The proposed methods showed a reliable, fast, and accurate measurement compared to other different image measuring softwares, such as ImageJ.
Highlights
The textile yarn is a group of fibers twisted together [1], and the diameters of the yarn and its fibers are important characteristics in the textile industry
The diameter distri‐ bution in Figure 10 shows an average diameter of a single fiber around 8.00 ± 0.64 μm obtained by the proposed algorithm compared to 8.06 ± 0.45 mm obtained by ImageJ
The yarn’s diameter method successfully eliminated the hairiness of the yarn and gave an average diameter of 0.47 ± 0.03 mm after applying the algorithm on the sample image compared to 0.42 ± 0.03 mm obtained manually by ImageJ software
Summary
The textile yarn is a group of fibers twisted together [1], and the diameters of the yarn and its fibers are important characteristics in the textile industry. Manal’s group used the image processing and machine learn‐ ing to determine the characteristics such as yarn tenacity, elongation%, and coefficient of mass variation% of the cotton’s yarn [13]. From these examples and many more in the ldeiafrfnerinengttoredseetaerrcmhinfieeltdhes,cihmaraagceteprirsoticcessssuincgh atescyhanrinquteensacpiotys,seelsosngcoatnisoind%er,aabnlde cimoepffiaccitentot oafchmieavses avuatroiamtiaotnic%anodf athcceucroattteoann’salyyasrisn. Ty,hbeinoathriezrinalggiotr,ifithllmingmiena-sbuertwesetehnevdoiaidms,etaenrdorfetmheoivnindgi‐ tvhieduhaalirfiibneersssooffathcreoyssa‐rsne.ctTiohneaol timheargaelgoof rthitehymarmn,etahseudreestetchteiodniaomf tehteecriorcfutlhaer oinrdciivrciudluaarl‐ filibkeercsroosfsa‐scercotsios-nsseoctfifoinbaerlsimisabgaeseodf othnethyearcnir,ctuhleardHetoeuctgihontroafnsthfoermcir[c1u6l]atrhoart ccairncufilnadr-lainkde ccrhoasrsa-csteecrtiizoencsirocfufilabreorsr niseabra‐sceirdcuolnarthsheacpirecsuilnaranHiomuagghe.trBaontshfoarlgmor[i1t6h]mtshaatrecadnepfilnoydeadnidn cahMarAacTteLrAizBe ecinrvcuirloanr morennet.ar-circular shapes in an image. Both algorithms are deployed in a MATLAB environment
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
