Research on the construction of a full-color gamut color mixing model and Friele color prediction algorithm for rotor spinning

Abstract

To achieve accurate color prediction of full-color gamut blended yarns, this paper constructs a full-color gamut grid color mixing model based on four primary color fibers: magenta, cyan, yellow, and gray. The Friele model color prediction algorithm was reconstructed by associating the model parameters with wavelength and blending concentrations. Next, 112 training samples and 7 validation samples were planned in the gridded model to be used for sample preparation and reflectance testing. Then, the gridded model was divided into 9 prediction regions, and the optimal model parameters were obtained by using the assignment iteration method at 31 wavelengths in each region, and a total of 279 model parameters were obtained, which were used as the basis for the full-color gamut color prediction. Finally, predictions of the color values and blending concentrations of the validation samples were conducted based on the model parameters of the region in which they were located, and the prediction accuracy of the algorithm was evaluated with color difference as a criterion. The results show that the mean color difference in the color value prediction of the validation samples is 1.324 and the mean color difference in the blending concentrations prediction is 0.84, indicating that accurate prediction of the full-color gamut blended yarns can be achieved.