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A dyed-absorbance-based framework for predicting and optimising reactive dye recipes in cotton fabric dyeing
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- Authors
- Ji-Yong Hwang, Hyun-Woo Oh
- Citation
- Coloration Technology, Early Access
- ISSN
- 1472-3581
- Publisher
- John Wiley & Sons
- Language
- English
- Type
- Journal Article
- Abstract
- This study proposes a data-driven framework for predicting and optimising reactive dye recipes in cotton fabric dyeing. The proposed approach introduces dyed-absorbance as an empirical, reflectance-derived spectral index for wavelength-resolved spectral representation, rather than as a direct physical absorbance. The system predicts dye combinations from dyed-absorbance spectra and subsequently estimates the corresponding concentrations. By integrating a Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model with regression-based mathematical correction grounded in wavelength-dependent dyed-absorbance behaviour, the system is intended to improve colour-matching accuracy and reduce the number of redundant dyeing trials. Unlike conventional computer colour-matching evaluation methods that rely on reflectance-derived K/S values and extensive empirical calibration dyeing libraries, the proposed approach is designed to reduce reliance on accumulated calibration datasets by formulating wavelength-dependent relationships between spectral features and dye concentration. The framework was evaluated in a representative production-derived colour-matching case using laboratory-scale dyeing. The CMC colour difference (ΔE) value decreased from 1.94 to 0.43, and metamerism indices under illuminants A and U3500 were reduced to 0.29 and 0.14, respectively. The proposed framework operates on standard computing platforms and is presented as a workflow for dye recipe prediction and correction.
- KSP Keywords
- Convolution neural network(CNN), Cotton fabric, Dye concentration, Fabric dyeing, K/S values, Laboratory-scale, Long Short-Term memory(LSTM), Matching accuracy, Matching evaluation, Mathematical correction, Regression-based