Colour Mixing Modelling and Simulation: Optimization of Colour Recipe for Carded Fibres

Abstract

Colour matching between carded and finished fibres is an important challenge for textile industry. The straightforward approach for mixing together some differently coloured fibres in order to obtain a blend of a desired colour is to perform a trial and error approach starting from a given colour recipe and optimizing it with several attempts. Unfortunately, dyeing process so as the carding procedure may result in a carded fibre whose colour is different from the desired one. As a consequence textile companies have to modify the original recipe in order to reduce the gap between the colour of the final product and the desired one. The present work describes a model able to simulate the colour mixing of fibres in order to assess the best recipe. The model consists in two modules: a "prediction module" predicts the colour of a blend obtained by mixing together several fibres; an "optimization module" is used to optimize the final recipe. The devised system has been tested for optimizing the recipe of a set of 200 blends. The mean error in predicting the blend colour is about 15% with a variance of 0.165. The time for optimizing the recipe is reduced by 92%.

Color matching of fabric blends: hybrid Kubelka-Munk + artificial neural network based method

“…As a final remark, the results demonstrate that both methods (K-M+ANN and practical chart) average a closer prediction when compared with the theoretical approach proposed in Ref. 13.…”

“…11,12 Nevertheless, even when M is accurately determined, the S-N model provides reliable results only for blends composed of a maximum of five to six differently colored fibers of the same material, thus limiting the approach to a lower number of cases since most companies mix together up to 15 to 20 differently colored fibers, often made of different materials such as wool, nylon, polyester, etc. 13,14 Therefore, although the above-mentioned theoretical approaches provide excellent results for predicting the color of turbid media, they may lead to unsuitable results in forecasting the reflectance factors of blends obtained by mixing precolored fibers. As a consequence, further experiments and studies need to be carried out to reach accurate recipe predictions.…”

“…This is demonstrated in Ref. 13, where a transfer function linking the color spectrum obtained by a linear combination of the spectra of each component with the measured reflectance values of a first-attempt blend is determined. The approach proved to be effective for blends composed of more than 15 components, since the average color difference between the predicted spectra and the real spectra of the carded fibers for an experimental set of blends is <0.55 for the CMCð2∶1Þ tolerancing system.…”

“…When the operators need to create a fabric composed of a combination of different materials, the transfer function defined in Ref. 13 cannot be used even for a small variation of the recipe.…”

Optimization of potential field method parameters through networks for swarm cooperative manipulation tasks

“…To find such a vector, that is, to define the optimal parameters values, a good option could be to apply any optimization algorithms available in literature. 19,20 In effect, for a given set of these parameters it is possible to simulate the whole trajectory; consequently, it is straightforward to build a dataset of different trajectories by testing different values, varying them within a given range. Unfortunately, since each simulation requires the setting of 10 input parameters, performing a full factorial experiment 21 is not recommended, even in case only two states are selected for each parameter (such an experiment would require 2 10 ¼ 1024 simulations to be achieved, involving more than 500 hours of computational time).…”