Artificial neural networks approach for prediction of CIELab values for yarn after dyeing and finishing process

Abstract

In textile products, color plays an influential role in changing fashion trends. The main challenge in dyeing processes is the achievement of the desired color output (CIELab value) through various recipe matching algorithms and programs. A color matching approach in this context uses the type of dyestuff, the color of pre-treated fabric, and the owf% value of dyestuff as input sets, and the recipe for dyeing and CIELab values as outputs. The fiber, yarn, and finishing parameters that could influence dyeing processes, however, are excluded from these approaches (based on Kubelka-Munk theory). This study aims to develop a model that can predict the CIELab values (L*, a*, and b*) of dyed yarns after package dyeing. In order to predict CIELab values, artificial neural network-based prediction models have been developed using the properties of cotton fiber used in yarn production (HVI-AFIS etc.), yarn spinning, dyeing and finishing process parameters as input sets. A study has also been carried out on how input sets are to be related to the optimization of a prediction model. In order to account for the variability of the input sets, two different prediction models have been developed. Both models yield low prediction errors for three outputs: L*, a*, and b*. Based on the predictions by the models, the difference between the actual values and the predicted values is found to be within acceptable tolerances for L*, a* and b*, and a large part of the 498 predicted test data have an absolute error deviation of less than 0.5.