Yarn Hairiness

Barella, A.

doi:10.1080/00405168308688897

Cited by 107 publications

(80 citation statements)

References 47 publications

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“…Various sources of light are used. Among well established photoelectric devices for yarn hairiness determination the most popular are: Shirley Yarn Hairiness Tester [4,46] which uses LCD beam, Zweigle Hairiness Tester [4,46,50] utilizing laser light and Uster Zweigle Hairiness Tester 5 [4,46,51] where infrared light source is applied. Photoelectric devices of this type provide high quality results for straight fibres clearly separated from the yarn core.…”

Section: Review Of Existing Approaches To Yarn Hairiness Measurementsmentioning

confidence: 99%

“…Yarn hairiness is a complex parameter. Due to its complexity, various measures have already been proposed for quantifying yarn hairiness [4][5][6]. However, it definitely depends on the fibres on the outer layer of the yarn that do not directly adhere to the core.…”

Section: Introductionmentioning

confidence: 99%

“…One of the key parameters defining textile yarn quality is its hairiness [4][5][6]. Hairiness arises from protruding fibre ends released from the yarn surface which can be divided into the protruding fibre ends and the looped fibres arched out of the yarn core.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image processing and analysis algorithms for yarn hairiness determination

Fabijańska

Jackowska-Strumiłło

2012

Machine Vision and Applications

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Yarn hairiness is one of the key parameters influencing fabric quality. In this paper image processing and analysis algorithms developed for an automatic determination of yarn hairiness are presented. The main steps of the proposed algorithms are as follows: image preprocessing, yarn core extraction using graph cut method, yarn segmentation using high pass filtering based method and fibres extraction. The developed image analysis algorithms quantify yarn hairiness by means of the two proposed measures such as hair area index and hair length index, which are compared to the USTER hairiness index-the popular hairiness measure, used nowadays in textile science, laboratories and industry. The detailed description of the proposed approach is given. The developed method is verified experimentally for two distinctly different yarns, produced by the use of different spinning methods, different fibres types and characterized by totally different hairiness. The proposed algorithms are compared with computer methods previously used for yarn properties assessment. Statistical parameters of the hair length index (mean absolute deviation, standard deviation and coefficient of variation) are calculated. Finally, the obtained results are analyzed and discussed. The proposed approach of yarn hairiness measurement is universal and the presented algorithms can be successfully applied in different vision systems for yarn quantitative analysis.

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Section: Review Of Existing Approaches To Yarn Hairiness Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image processing and analysis algorithms for yarn hairiness determination

Fabijańska

Jackowska-Strumiłło

2012

Machine Vision and Applications

View full text Add to dashboard Cite

show abstract

“…This is usually measured in terms of the number of protruding hairs per unit length and the length of hairs. Numerous measurement techniques have been developed [2,3]. These are mainly based on the use of photographic, microscopic, photoelectric and optical techniques.…”

Section: Yarn Characterisationmentioning

confidence: 99%

“…Fibre hairs surrounding a textile yarn may adversely affect the operations which follow spinning, such as weaving and knitting, and may influence the characteristics of the finished product [2,3]. Hence, measurement of yarn hairiness has become an essential and routine test in the textile industry.…”

Section: Introductionmentioning

confidence: 99%