Optimization of the Dyeing Process and Prediction of Quality Characteristics on Elastic Fiber Blending Fabrics

This study aims to find the optimal conditions for dyeing polyester (PET) and Lycra®-blended fabric and predict the quality characteristics, where PET and Lycra®-blended fabric were taken as raw material with dispersed dyes using a one-bath two-section dyeing method, characterizing the color strength of gray fabric. Adopting the Taguchi method for parameter design, machine working temperature, dyeing time, dye concentration, and bath ratio, which have an influence on dyeing, were chosen as control factors to conduct experiments using orthogonal arrays, and analysis of variance was incorporated to determine optimal processing conditions, significant factors, and percent contribution. With the smaller-the-better characteristic for color strength of gray fabric as the target characteristic, calculations were conducted to confirm the reproducibility of the experiment. We found from experimental results that color strength for gray fabric dyed under optimal conditions was closer to the target value. In addition, we constructed a prediction system based on the factors significantly influencing dyeing performance by a integrating genetic algorithm (GA) with a back propagation neutral network (BPNN), so as to find optimal connection weight values. Hence, learning algorithm efficiency was enhanced with decreased dependency on initial conditions, together with a more robust learning algorithm, so as to predict color strength for gray fabric. Moreover, improvements of BPNN, which is most widely used at present, will be influenced by choice of initial learning parameters more often than not, hence there is a disadvantage in that it is probable that only local optimal solutions are found instead of global optimal solutions.