Yarn Diameter and Linear Mass Correlation

Carvalho, Vı́tor; Vasconcelos, Rosa; Soares, Filomena; Belsley, M.

doi:10.1007/s10921-009-0046-8

Cited by 17 publications

(16 citation statements)

References 8 publications

(3 reference statements)

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“…Considering the optical resolution of the yarn diameter and hairiness measurements and the known relationship between yarn linear mass (tex(g/km)) and diameter (m) (diameter=0.060√tex) [5]), it is possible to correctly determine the traditional yarn parameters using only optical sensors [5,6]. In order to compare these two systems a 100% cotton 295 g/km linear mass yarn (Figure 4) was evaluated.…”

Section: Resultsmentioning

confidence: 99%

A comparison of mass parameters determination using capacitive and optical sensors

et al. 2009

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This paper presents a comparison study between the use of 1mm resolution capacitive and optical sensors for the determination of yarn mass parameters. A parallel plate capacitive sensor to determine yarn mass variations and a yarn diameter and hairiness determination solution using optical sensors and integrating optical signal processing based on Fourier analysis are described. As there is a high correlation between yarn diameter and yarn mass, it is possible to determine yarn mass and infer variations in yarn diameter and vice-versa. Moreover, by optically detecting the degree of yarn hairiness, one can quantify its influence on the capacitive sensor mass variation measurements. Here we present the results of a signal processing analysis and statistical description of measurements carried out on a 100% cotton 295 g/km linear mass yarn. We conclude that an accurate yarn characterization can be carried out using optical sensors alone, reducing systems cost, complexity and increasing efficiency.

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Section: Resultsmentioning

confidence: 99%

A comparison of mass parameters determination using capacitive and optical sensors

et al. 2009

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“…Moreover they were encumbered with an error due to problems with identifying the boundary of yarn. Recently, modern high quality electron microscopes are used for yarn hairiness measurements [12,13]. These microscopes often provide digital images of the yarn, therefore microscopic methods demand development of image processing and analysis algorithms.…”

Section: Review Of Existing Approaches To Yarn Hairiness Measurementsmentioning

confidence: 99%

“…For more than 30 years computer vision techniques have been used in textile science for yarn quality inspection [3,27,49]. In modern computer vision systems image processing and analysis algorithms are used for an automatic measurement of important yarn quality parameters, such as hairiness [12,15,20,22,39,40], diameter [13,38], twist [17,41], thickness [17,49], faults [14], density and bulkiness [15], surface defects [29], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Several image-based approaches dedicated to yarn properties assessment have already been proposed. However, these methods use various kinds of thresholding approaches for yarn core and protruding fibres segmentation [7,[12][13][14][15][16][17][18][19][20]22,27,29,[31][32][33]35,[38][39][40][41]52,54], which often lack universality as they either are dedicated to the certain class of images provided by the certain vision system or require certain yarn orientation and experimental setting of parameters by trial and error examination. This paper presents application of graph based method for yarn core extraction and high pass filtering based method for yarn segmentation.…”

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

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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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“…Carvalho et al [17] focused on the determination of the statistical correlation between yarn diameter and yarn linear density. The experimental methods were based on optical analysis and on the image processing techniques applied to electron microscope images.…”

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confidence: 99%