Color prediction models for digital Jacquard woven fabrics

Chae, Younbyoung; Xin, John Haozhong; Hua, Tao

doi:10.1002/col.21945

Cited by 18 publications

(22 citation statements)

References 8 publications

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“…The relative value method is to select one of the monochromatic fibers in the two-component blending materials, 15,16 and S of the selected fiber at each wavelength is set to 1, that is S 1 = 1, then K 1 = (K/S) 1 . Because the required sample is a two-component sample when using the relative value method, Equation (2) can be expressed as:…”

Section: Least Squares Methodsmentioning

confidence: 99%

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K‐M theory of fabric knitted by three‐channel rotor spun wool yarn

Yang

Deng

et al. 2018

Color Research & Application

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Two‐component and three‐component color blended yarns were spun by red, yellow, and blue wool slivers using a three‐channel rotor spun machine, and the corresponding plain fabrics were knitted. The color‐matching models of K‐M theory were built with the relative method and the least squares method, respectively. Colors and blending ratios of the fabrics were predicted by the model. The results showed that the average color differences of the samples predicted by the two methods are both about 1.0 and the mean value of the proportional error is below 3%. The least squares method has a better color‐matching effect for the three‐component sample, and the relative value method has better color‐matching results for the two‐component sample. When the tolerance range is 2.0, the pass rates of the samples predicted by either the relative value method or the least squares method reach 100%.

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Section: Least Squares Methodsmentioning

confidence: 99%

“…Color blended wool yarns have a special variety of color styles meeting the demand of consumers for fashion and personalization of apparel fabrics. 1,2 Its production is environmentally friendly, small-scale, and multi-style. In addition, it can be woven or knitted as fabric without dyeing.…”

Section: Introductionmentioning

confidence: 99%

K‐M theory of fabric knitted by three‐channel rotor spun wool yarn

Yang

Deng

et al. 2018

Color Research & Application

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“…In addition, using regression methods, they determined relationships between theoretical color values with linear behavior and the spectrophotometrical color values of bicolor woven fabrics in CIELAB color space [5][6][7]. Chae et al proposed geometrical and colorimetrical modeling for single-layered colorful woven structures with improved accuracy in color prediction [8][9][10]. In addition, the effect of neighboring color on color perception was investigated for color appearance modeling of multi-colored woven fabrics [11].…”

Section: Introductionmentioning

confidence: 99%

Color Attributes of Colored-Yarn Mixed Woven Fabrics Made of Raw-White Warps and Multicolored Wefts and Based on Weft-Backed Structures

et al. 2018

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This paper reports the development of colored-yarn mixed woven fabrics by using raw white warps and multicolored-wefts, as well as a study of the influential factors on the color attributes of the resultant fabrics. Weft yarns in six colors, together with the white warp yarns, were used to create a series of fabric colors. Two types of new weft-backed structures were designed to assign the desired wefts for color mixing, as well as to reduce the white warp floats on the surface and thus, the lightness of the fabric. The effects of the proportion of yarn color components, weft density, and the introduction of black weft floats on the color attributes of fabrics, were investigated. The results show that through varying the proportion of mixing yarn color components, via fabric structure, a series of mixed red-blue and green-yellow colors for fabrics are created, respectively. Colored yarn mixed fabric presents a lowered lightness after a middle regulating layer is introduced into the structure. Compared to fabrics with a lower density, higher density fabrics possess lower lightness, higher redness and blueness in the blue-red fabrics, and higher greenness and yellowness in the yellow-green fabric. The lightness of fabric lowers after adding black yarn.

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“…All the above research were conducted based on blending fibers during drafting process or open‐cleaning process or by hand . Actually in textile industry, blending methods also have great influence on the colors of blended fibers.…”

Section: Introductionmentioning

confidence: 99%

“…All the above research were conducted based on blending fibers during drafting process or open-cleaning process or by hand. [14][15][16][17] Actually in textile industry, blending methods also have great influence on the colors of blended fibers. Until now no such work related to color prediction was taken on blending fiber during spinning process especially for rotor spun yarn.…”

Section: Introductionmentioning

confidence: 99%

Color matching of fiber blends: Stearns‐Noechel model of digital rotor spun yarn

Yang

Han

et al. 2017

Color Research & Application

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Stearns‐Noechel model was utilized as a primary reference to study color matching principles of digital rotor spun yarn. Three primary colored (red, yellow and blue) cotton fibers were used to spin blended yarns. Spectral reflectance of the two‐component and three‐component samples was measured with data color spectrophotometer. For these samples, the Stearns‐Noechel model parameter M was determined. Four methods were employed to calculate the M value to improve accuracy of the model, 1.Classical method, named as M1; 2.Optimizing the M1 value obtained by the classical method considering the wavelength factor, named as M2; 3.Simplified M2 according to the linear correlation with the wavelength, named as M3; 4. Simplified M2 according to the segmentation correlation with the wavelength, named as M4. The study shows that average color difference of the two‐component decreases from 2.7 to 1.48, and for three‐component samples from 3.32 to 1.66, by using M2 instead of M1. While calculated using M3, the color difference of the two types of samples will be 1.73 and 2.19, correspondingly. This cannot meet color matching needs. As for M4, the average color difference of the two categories will be 1.54 and 1.91, better than the result obtained using M1 and M3, worse than M2.

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Color prediction models for digital Jacquard woven fabrics

Cited by 18 publications

References 8 publications

K‐M theory of fabric knitted by three‐channel rotor spun wool yarn

K‐M theory of fabric knitted by three‐channel rotor spun wool yarn

Color Attributes of Colored-Yarn Mixed Woven Fabrics Made of Raw-White Warps and Multicolored Wefts and Based on Weft-Backed Structures

Color matching of fiber blends: Stearns‐Noechel model of digital rotor spun yarn

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