Development of shade prediction system to quantify the shade change after crease recovery finish application using artificial neural networks

Abstract

Finishes are applied to improve the look, performance and feel of the fabrics. Crease recovery finishes form a three-dimensional crosslinking network on the surface of the cotton knitted fabric to control its dimensions. However, application of the crease recovery finishes induces the shade change in the dyed fabrics. This paper presents the phenomenon of shade change for different colors and shade percentages and use of artificial intelligence-based prediction system to foresee the behavior of shade after finish application. The individual neural networks were trained for the prediction of color of the finished samples, which are delta color coordinates values (△L, △a, △b, △c & △h). The input variables, i.e. reflectance values (Visible ranges 400–700 nm) of dyed samples, color, shade percentage and finish concentration were used to train the networks. The trained neural networks were validated through ‘cross validation’ and ‘hold out’ techniques. The shade prediction model was developed by combining the individually trained artificial neural networks and the developed model can predict the shade change with more than 90% accuracy. This will help the dyers to predict shade change prior to dyeing & finishing and they will adjust their recipe accordingly, which can ultimately reduce the rework and reprocessing in the textile wet processing industries.

Keywords:

• Artificial neural networks
• CMC value
• resilient back propagation
• shade change prediction
• training and testing performance

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors highly acknowledge the Higher Education Commission of Pakistan for providing financial support to complete this research work under Technology Development Fund Project TDF-097 and Kays & Emms Pvt. Ltd. for being industrial partner in the project.