A multi-scale color prediction model for color blended spinning products based on fibre-yarn-fabric interlayer structure effect

Predicting the color of blended spun products remains an arduous challenge, which hinders practical application, due to the effects of the material structure. To solve this issue, this paper proposes a new structural spectral color prediction method based on the single-constant Kubelka-Munk (KM-1) model, here named the multiscale spectral color prediction model. This method involves fiber-yarn and yarn-fabric reflectance structure and Lab space spectra transfer model, building upon our previously proposed color and non-color prediction method. Significantly, by introducing structural twisted yarns between fabrics knitted from parallel fibers, only five groups of different monochromatic materials and 14 color-blended fibers were needed to train this model. This greatly reduces the number of training samples and improves prediction accuracy. Experimentally, the average and maximum color difference of 87 blended yarn samples were only 0.65 CIEDE2000 units and 1.56 CIEDE2000 units, markedly lower than those of the KM-1 model (~6.18, ~12.26), two-constant KM (KM-2) model (~1.33, ~9.50), Friele model (~1.62, ~3.06), and Stearns-Noechel (S-N) model (~1.14, ~3.51). Moreover, the average and maximum color difference of 87 blended fabric samples were 0.54 and 1.25, smaller than those of the KM-1 model (~6.01, ~12.27), KM-2 model (~1.17, ~8.94), Friele model (~1.29, ~2.31), and S-N model (~1.02, ~4.09). The results indicate that the optimization model has good performance and can be used to predict the color of mixed color-matched spun products.