Prediction of the Color and of the Color Solidity of a Jigger-dyed Cellulose-based Fabric: A Cascade Neural Network Approach

Abstract

The color and the color stability of a fabric dyed with a jigger machine are highly dependent on the color of the dyes and on technological parameters, such as the process temperature, pH of the dyebath, and time of dyeing. On the basis of their skill, colorists are capable of choosing the technological parameters and the recipes for dyes in order to obtain the desired dyed fabric. However, they need to perform many tests before reaching the preferred product. As a matter of fact, a straightforward relationship between the color of dyes, process parameters, and final color and color solidity is not found in literature. The present work aims to help the colorist in predicting the color and the color stability of the fabric, without the needing to physically dye it. In detail the paper describes a tool, based on the Cascade Neural Network (CNN), that is able to receive the value of some colorimetric and technological parameters as input and to predict, as output, the color appearance and the color solidity of a fabric dyed with a jigger machine. The CNN is composed of a “color prediction module” (ANN I) and a “color solidity prediction module” (ANN II) that work in cascade. The CNN has been validated by means of a large set of experiments. The mean error between the color prediction and the real values of the dyed fabric, in terms of CIE 1976 (L ^*, a ^*, b ^*) color distance, is equal to 0.47 with a variance of 0.031. The maximum color solidity prediction error is 0.5.