Color difference of yarn-dyed fabrics woven from warp and weft yarns in different color depths

Abstract

Purpose This paper aims to determine the most practically applicable color-difference formula for yarn-dyed fabrics woven from warp and weft yarns in different color depths and to establish color-difference tolerance for perceptibility by evaluating yarn-dyed fabrics visually and instrumentally. Design/methodology/approach A total of 108 sample pairs were evaluated by a panel of 13 observers with perceptibility method under three typical light sources (A, D65 and cool white fluorescent). The data sets were statistically analyzed by the homogeneity of variance test (F-test), analysis of variance, standardized residual sum of squares and performance factor/3. Findings Light sources had a slight influence on the visual assessments of yarn-dyed fabrics. Among the eight color-difference formulae for measurements of yarn-dyed fabrics, CIEDE2000(2:1:1) outperformed all other tested formulae, and the color tolerance for the perceptibility of CIEDE2000(2:1:1) was 0.62. When the homochromy index (K) of warp and weft yarns of yarn-dyed fabric was lower than 1.25, the color difference based on ΔE*00(2:1:1) between the two samples was acceptable in terms of the color tolerance for perceptibility (i.e. 0.62). Practical implications The warp and weft yarns in different color depths could be woven in fabric with a relatively uniform color appearance. Originality/value This study could contribute to cost savings by reusing disqualified dyed yarns during the weaving manufacturing process.

Evolving regularised random vector functional link by seagull optimisation algorithm for yarn‐dyed fabric colour difference classification

“…At present, the detection range of colour difference meters is small and the overall uniformity is poor. Therefore, machine vision‐based colour difference detection and classification of printed and dyed fabrics are widely applied in the textile field 6–8 . The intelligent realisation of textile colour difference detection and classification is of great significance to improve the accuracy, speed and efficiency of dyeing and dyeing fabric machine vision colour difference detection, which is helpful to improve the dyeing and dyeing level of textile enterprises and meet the needs of the development of textile industry automation.…”