Visual colour fastness assessments for multi-coloured fabrics

Kuramoto, Kei; Yoshihisa, M; Kitaguchi, Saori; Satô, Tetsuya

doi:10.1088/1757-899x/254/14/142014

Cited by 3 publications

(5 citation statements)

References 2 publications

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“…All remaining linen/silk fabric samples printed in pigment and reactive dyes demonstrated dyeing fastness relative to various impacts (dry friction, perspiration, and washing) and could be assigned maximum points, e.g., 5 points. These results prove the stainless quality of the implementation of finishing [11,15,16,20]. This means that dye migration was very low in this case because dyeing, printing, and scouring were performed at high quality levels.…”

Section: ×supporting

confidence: 59%

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Analysis of Color Fastness and Shrinkage of Dyed and Printed Linen/Silk Fabrics

et al. 2022

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In this study, we analyzed the impact of the dyeing and the digital printing processes of linen/silk fabric (70% linen/30% silk) on its color fastness and shrinkage in the directions of warp and weft. This is a highly relevant topic because a single fabric combines two natural fibers of different origins—silk, a protein, is animal-based, whereas linen is derived from cellulose and is, thus, plant-based. Therefore, the different natures of the two fibers determine distinctive chemical properties. As a result, selecting the optimal technological course of processing for blended yarn fabrics and their yarn is a challenging task. The quality of the conducted finishing is determined by the physical properties of fabrics, such as color fastness to perspiration; soap; dry friction and wet friction; the pH of the fabric after finishing; and the shrinkage of the fabric in the directions of warp and weft. Here, color fastness was assigned the top grade with respect to all potentially harmful factors, except for wet friction. The evaluation proves that the technological regimes employed for digital printing and dyeing in the production item’s fabrics were properly selected.

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Section: ×supporting

confidence: 59%

“…For example, cellulose fibers dyed in reactive dyes are characterized by exceptional color fastness. Protein origin fibers dyed in acidic and reactive dyes are also well known for excellent color fastness [14,15].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Color Fastness and Shrinkage of Dyed and Printed Linen/Silk Fabrics

et al. 2022

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“…The results obtained from experienced and inexperienced observers were statistically analyzed. The results indicated that the visual ratings of experienced and inexperienced observers were both around one rating higher than the instrumental ratings 3 . In McLaren's research, 10 light fastness panels were examined by 70 observers.…”

Section: Introductionmentioning

confidence: 91%

“…In his article, the paired t test showed a significant difference between the two observer groups and STRESS (standardized residual sum of square) results also indicated an increase in the accuracy of the inexperienced observers due to the repetition of the test 2 . Kuramoto et al, 3 also, investigated colorfastness in terms of the change in color of multi‐colored fabrics. The results obtained from experienced and inexperienced observers were statistically analyzed.…”

Section: Introductionmentioning

confidence: 99%

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A neural network‐based instrumental method for light fastness assessment with dataset validation

Karami

Izadan

Hamadani

2020

Color Research & Application

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A novel instrumental method for the light fastness assessment has been established. The accuracy of the visual method is affected by undesirable constraints, such as the different severity and the complications due to off-tone color change of the specimens. Thus, it was desired to develop an instrumental method of the light fastness assessment. In this regard, a neural network-based instrumental method of the light fastness assessment was developed. First, a proper light fastness panel was prepared, and then the visual light fastness assessments of experienced and inexperienced observers were collected. The accuracy and repeatability of the visual assessment results from these two groups of the observers were analyzed using different statistical criteria. The statistical analysis has shown that the mean of three trials of the inexperienced observers can be combined with the mean of the results obtained by experienced. Thus, all the results from the inexperienced and experienced observers were used to prepare a valid dataset for training a neural network. Different neural network structures trained with the prepared valid dataset. Among all of the implemented structures, the most accurate neural network structure is the one with one hidden layer and a 3-7-1 structure. The root mean square error and correlation coefficients of proposed 3-7-1 NN are 0.32 and 0.975, respectively, for the test sets. According to these results and the results from the comparison of the instrumental and visual assessment of the light fastness, it was concluded that the proposed neural network can be used for the instrumental light fastness assessment.

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