A neural network algorithm and its prediction model towards the full color phase mixing process of colored fibers

Sun, Xianqiang; Liu, Yuexing; Wang, Liqiang; Liu, Lixia

doi:10.1177/00405175221138978

Cited by 4 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of computer algorithms for color matching and color prediction has many successful cases, Jianguo R [3] and other scholars used polynomial regression algorithms to optimize the color matching of camouflage design, resulting in a minimum color difference of 1.691 and a maximum color difference of 2.497. Sun Xianqiang [4] and other scholars used neural network prediction model to predict the full-color mixing of colored fibers, showing that the average color difference was 1.691, the average color difference was 2.497, and the average color difference was 2.497. Gao Jiale [5] and other scholars in 2023 based on chaotic sparrow algorithm to match the color of fur materials, the average value of the color difference is 0.105.…”

Section: Introductionmentioning

confidence: 99%

Research on Textile Dyeing Formulation Based on Young's Double-Slit Interference Experiment Optimization Algorithm

Tam,

Chen,

2024

HSET

View full text Add to dashboard Cite

With the development of intelligent algorithms, computer color matching has become an important method to improve product quality and save labor cost in printing and dyeing industry. In this paper, based on the K-M optical theory and the color difference theory of CIELAB uniform color space, and combined with the latest heuristic optimization algorithm "Young's Double-Slit Interference Experiment Optimizer (YDSE)" proposed in 2023, a feasible solution to generate textile dyeing formulas quickly is proposed. The model is tested with 12 textile target samples, and the experiments show that the proposed dyeing formulation model can converge quickly, and the color parameters of the generated scheme are closer to those of the real samples, with the maximum parameter error not exceeding 10-2 orders of magnitude, and the predicted color differences of the optimal scheme are all in the range of 10-6 orders of magnitude or less. Simultaneous testing of the sample formulations using three other common optimization algorithms, WOA, HHO, and IHHO, shows that their predicted color differences are greater than those of YDSE, proving the reliability of the model.

show abstract

Section: Introductionmentioning

confidence: 99%

Research on Textile Dyeing Formulation Based on Young's Double-Slit Interference Experiment Optimization Algorithm

Tam,

Chen,

2024

HSET

View full text Add to dashboard Cite

show abstract

Research on full‐colour gamut matching of wool blended yarn based on the Kubelka–Munk prediction algorithm

Zhu,

Xue,

Xue

et al. 2024

Coloration Technology

View full text Add to dashboard Cite

This article aims to solve the problems of a high inventory of coloured fibres, low efficiency of manual colour matching and poor repeatability in the woollen textile industry. Seven primary colour fibres were prepared based on seven selected primary colours, namely, red, yellow, green, cyan, blue, magenta and grey, which were then divided into six groups of ternary primary colour fibres. Next, six ternary coupling‐combination grid colour‐mixing models were constructed and merged into a full‐colour gamut grid colour‐mixing model. Based on this, 241 types of blended yarns and knitted fabrics were prepared. A colour‐matching system for wool colour‐spun yarns was constructed based on the Kubelka–Munk colour prediction algorithm and the full‐colour gamut grid colour‐mixing model. Training samples, test samples and validation samples are planned in each colour‐mixing area for constructing the colour‐matching system and validating its predictive performance. The results of spinning experiments show that full‐colour gamut spinning within the mixing range of seven primary colour fibres is achieved based on the constructed full‐colour gamut grid colour‐mixing model. The predicted results of test samples and validation samples show that the average colour difference between the predicted colour value and the measured colour value is 0.642, and the average error formed by the predicted blending ratio and the actual blending ratio of primary colour fibres is 0.0223. The results indicate that the constructed colour‐matching system achieved an accurate prediction of colour values and the blending ratio of primary colour fibres for blended yarns.

show abstract

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

Ingle,

Jasper

2024

Textile Research Journal

View full text Add to dashboard Cite

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.

show abstract

A neural network algorithm and its prediction model towards the full color phase mixing process of colored fibers

Cited by 4 publications

References 25 publications

Research on Textile Dyeing Formulation Based on Young's Double-Slit Interference Experiment Optimization Algorithm

Research on Textile Dyeing Formulation Based on Young's Double-Slit Interference Experiment Optimization Algorithm

Research on full‐colour gamut matching of wool blended yarn based on the Kubelka–Munk prediction algorithm

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

Contact Info

Product

Resources

About