Application of genetic algorithm to color recipe formulation using reactive and direct dyestuffs mixtures

Abstract

Color reproduction is a science in constant development. In this article, a new model to solve the color recipe prediction problem using a genetic algorithm is proposed. The objective is to optimize the color recipe prediction stage by determining the dyes to use in a mixture and their respective proportions to reproduce the target color. Two ranges of dyes were used for dyeing 100% cotton woven fabrics: three reactive dyes (CI Reactive Red 238, CI Reactive Yellow 145, and CI Reactive Blue 235) and four direct dyes (CI Direct Orange 34, CI Direct Red 227, CI Direct Blue 85, and CI Direct Black 22). The criterion of optimization, in reproducing the desired shades, is to minimize the CMC color difference between the desired reference color and the color resulting of the predicted recipe. The proposed algorithm revealed good results with small CMC color differences between target and reproduced colors. The effectiveness of the algorithm was also evaluated and proven by calculating errors between the predicted concentrations in the proposed recipes and the actual concentrations.

An Industrial Dyeing Recipe Recommendation System for Textile Fabrics Based on Data-Mining and Modular Architecture Design

“…Calibration between individual dye concentrations and the color must be performed. Chaouch et al [8] (1) Using of genetic algorithm.…”

“…Recently, many efforts have been made towards developing better recipe recommendation methods for the dyeingprinting industry using modern information techniques [8]- [9]. These existing works have made important contributions for increasing the accuracy in the recipe prediction.…”

Towards Better Data Pre-Processing for Building Recipe Recommendation Systems from Industrial Fabric Dyeing Manufacturing Records: Categorization of Coloration Properties for a Dye Combination on Different Fabrics

“…With the development of modern information techniques, the using of data-mining approaches, which makes full use of historical dyeing data to find the quantitative relations between dye concentrations and the color, has now become a new direction for the development of dyeing recipe recommendation systems [6][7][8][9][10][11]. The dyeing data can either from laboratories, which are usually recorded in professional and standard ways, or from the manufacturing industry, which are usually not well organized.…”

Textile color formulation using linear programming based on Kubelka‐Munk and Duncan theories

“…Other methods that are based on artificial intelligence techniques such as neural networks, genetic, and ant colony algorithms have also been proposed and applied for the color formulation of textile samples. [15][16][17][18][19][20][21][22] In this article, we proposed a new optimization method for color matching using linear programming (LP) optimization. Two ranges of reactive dyestuffs mixtures were used for dyeing cotton woven fabrics.…”