Prediction and modeling of tie-dye polyester fabric's texture and color feature in supercritical carbon dioxide

Abstract

To mitigate the water-intensive challenges of tie-dye clothing production and address the absence of objective evaluation for finished products, we have explored the correlation between tie-dye techniques in supercritical carbon dioxide (SC-CO2) and image patterns. Employing digital image processing, we extracted the average HSV three-component value of the valid tie-dye area, the proportion of incompletely dyed area in the HSV color space, and the initial three texture features of the digital tie-dye image using the Tamura method. We scrutinized five factors in the supercritical CO2 dyeing process: rotational speed, dyeing temperature, dyeing pressure, dye concentration, and dyeing time. The single-factor climbing test revealed that temperature and pressure are the predominant factors influencing tie-dyeing performance. We propose a correlation between the texture parameters of tie-dyeing and the swelling effect of polyester fabric in a supercritical CO2 environment. Subsequently, we conducted two-factor tests on temperature and pressure to investigate their specific effects on the parameters of supercritical polyester tie-dyeing. The results indicated significant changes in regularity, saturation, and the proportion of undyed area with variations in dyeing temperature and pressure. Building upon these findings, we developed an artificial neural network for predicting tie-dye dyeing temperature and pressure in supercritical CO2, utilizing features regularity, saturation, and proportion of undyed area as inputs. In the 105st round, experimental results demonstrated an accuracy of 0.99445, substantiating the efficacy of tie-dye pattern features in supercritical CO2 for technique forecasting.