Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/34117
Title: Color prediction models for digital Jacquard woven fabrics
Authors: Chae, Y
Xin, JH 
Hua, T
Keywords: Color appearance
Color mixing model
Color prediction
Digital Jacquard
Model optimization
Issue Date: 2015
Publisher: John Wiley & Sons
Source: Color research & applications, 2015 How to cite?
Journal: Color research & applications 
Abstract: The current industry practice for producing jacquard fabrics uses computer-aided design (CAD) systems that provide visual simulations of the final color appearance of actual fabrics prior to production. This digital process is fundamentally based on the prediction of combined weave-color effects, which can be successfully achieved by accurate color mixing models and the structural details of the fabrics. With the accurate models used in CAD systems, designers would see simulations more closely resembling fabrics to be produced. By checking the previews, the designers can easily modify, that is, recolor, the designs on the display monitor without doing repetitive physical sampling with the adjustment of the weaves and the yarn colors. However, there is no ready applicable accurate color mixing model for woven structures and there has not been sufficient investigation of the color prediction despite its usefulness for the current digital CAD process. Our study investigated the, color prediction of jacquard woven fabrics designed based on the principle of optically subtractive color mixing with the use of CMY colors. The color prediction was firstly done through the application of the six color mixing models previously developed for various other applications including fiber blending and printing. The performance of each model was evaluated by calculating the difference between the predicted and the measured colorimetric data, using ΔECMC(2:1). The average color difference from the models was 11.93 ΔECMC(2:1), which is hardly acceptable in textile industry. In order to increase the accuracy in color prediction, the six models were then optimized. As a result, substantial improvements for all models were obtained with a decrease in color difference to 4.83 ΔECMC(2:1) on average after the optimizations. Among the six optimized color mixing models, the optimized Warburton-Oliver model, that is, W-O model, was found to have the lowest average ΔECMC(2:1) value of approximately equaling to 2, which is considered potentially useful to be applied to the current digital fabric color prediction.
URI: http://hdl.handle.net/10397/34117
ISSN: 0361-2317
DOI: 10.1002/col.21945
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