Prediction of parameters in the laser fading process of denim using convolutional neural networks

Tong, Yiyang; Wang, Qian; Wang, Zhen; Liang, Zihui; Xu, Qian; Zuo, Dunwen; Yi, Hong

doi:10.1177/00405175231162643

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…Additionally, one to two washes are required to remove any remaining indigo from the fabric surface after the color is removed by ozone, which conserves water because ozone requires fewer washes overall. A popular chemical technique for producing color-fading effects in denim is ozone [18,50].…”

Section: Sustainable Benefits Of Lasermentioning

confidence: 99%

Denim Manufacturing and Washing as a Fashioned Garments

Hassabo

Hegazy

Elmorsy

et al. 2023

J.Text.color. pol. sci.

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n the last three decades, denim has been the most preferred material for dresses. Denim has waded into the acceptance of children, women, and men, from special wear to everyday wear. The current fashion shows that consumers are interested in wearing denim and believe it to be a comfortable material for clothing. In the 1980s, when denim was first introduced to the market, an effort was made to create cotton denim-style clothing using just 100% polyester threads. Denim is currently one of the most popular cotton dress fabrics, but in the future, blends may play a bigger part in denim.

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Section: Sustainable Benefits Of Lasermentioning

confidence: 99%

Denim Manufacturing and Washing as a Fashioned Garments

Hassabo

Hegazy

Elmorsy

et al. 2023

J.Text.color. pol. sci.

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Optimisation of laser fading process in denim trousers: An industrial scale approach

Atav,

Gündüz,

Yaz

et al. 2023

Coloration Technology

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Colour fading with laser application has now become a well‐known and frequently used process in the denim industry. However, in practice, high strength losses and even tears may occur in the trousers as a result of incorrect or excessive laser application. In this study, the effects of resolution (dpi) and pixel time (μs), which are the two main parameters of laser colour fading, and also the effects of two‐passage laser treatments on colour fading and fabric strength were examined statistically and optimum conditions were determined. When the results of the studies are evaluated in general, it can be said that as the resolution (dpi) increases during the laser process, the colour fading effect increases, but when it is above the 31 dpi value, the strength loss increases excessively. As the second important parameter, the pixel time, increased, the colour fading effect increased. When the time exceeds 80 μs, there is a marginal increase in the strength loss value. Furthermore, it was observed that colour fading (%) values increase if laser process is applied in two passages, but in this case also the tear strength loss increases. It can be said that optimising the laser processing conditions in enterprises according to the product type (fibre content, weight, etc.) is a more accurate approach in terms of both the fading performance to be obtained and product quality. For 11.25 ounce trousers made of 95.3% cotton/3.4% polyester (T400)/1.3% elastane used in this study, it can be said that laser application could be made in two passages if necessary for obtaining high colour fading, however it should be noted that resolution and pixel time should not exceed 31 dpi and 80 μs, respectively. Provided that resolution and pixel time does not exceed 31 dpi and 80 μs, respectively, they can be changed depending on the degree of colour fading needed.

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A review of deep learning and artificial intelligence in dyeing, printing and finishing

Ingle,

Jasper

2024

Textile Research Journal

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This review focuses on the transformative applications of deep learning and artificial intelligence in textile dyeing, printing, and finishing. In textile dyeing, the topics span color prediction, color-based classification, dyeing recipe prediction, dyeing pattern recognition, and the nuanced domain of color fabric defect detection. In textile printing, applications of artificial intelligence and machine learning center around pattern detection in printed fabrics, the generation of novel patterns, and the critical task of detecting defects in printed textiles. In textile finishing the prediction of fabric thermosetting parameters is discussed. Artificial neural networks, diverse convolutional neural network variations like AlexNet, traditional machine learning approaches including support vector regression, principal component analysis, XGBoost, and generative artificial intelligence such as generative adversarial networks, as well as genetic algorithms all find application in this multifaceted exploration. At its core, the interest to use these methodologies is because of the need to minimize repetitive and time-consuming manual tasks, curtail prototyping costs, and promote process automation. The review unravels a plethora of innovative architectures and frameworks, each tailored to address specific challenges. However, a persistent hurdle looms – the scarcity of data, which remains a significant impediment. While unveiling a collection of research findings, the review also spotlights the inherent challenges in implementing artificial intelligence solutions in the textile dyeing and printing domain.

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Prediction of parameters in the laser fading process of denim using convolutional neural networks

Cited by 4 publications

References 34 publications

Denim Manufacturing and Washing as a Fashioned Garments

Denim Manufacturing and Washing as a Fashioned Garments

Optimisation of laser fading process in denim trousers: An industrial scale approach

A review of deep learning and artificial intelligence in dyeing, printing and finishing

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