A Controlled Study on Machine Learning Applications to Predict Dry Fabric Color from Wet Samples: Influences of Dye Concentration and Squeeze Pressure

Most dyeing occurs when a fabric is in a wet state, while color matching is performed when the fabric is in a dry state. As water is a colorless liquid, it has been difficult to analytically map these two states using existing color theories. Machine learning models provide a heuristic approach to this class of problems. Linear regression, random forest, eXtreme Gradient Boosting (XGBoost), and multiple neural network models were constructed and compared to predict the color of dry cotton fabric from its wet state. Different models were developed based on squeeze pressure (water pickup), with inputs to the models consisting of the L*a*b* (L*: lightness; a*: red–green axis; b*: blue–yellow axis) coordinates in the wet state and the outputs of the models consisting of the predicted L*a*b* coordinates in the dry state. The neural network model performed the best by correctly predicting the final shade to under a 1.0 color difference unit using the International Commission on Illumination (CIE) 2000 color difference formula (CIEDE2000) color difference equation about 63.9% of the time. While slightly less accurate, XGBoost and other tree-based models could be trained in a fraction of the time.