Research on the construction of a full‐colour‐gamut colour prediction system based on the Friele algorithm and rotor colour spinning

To achieve full‐colour‐gamut colour prediction for blended yarns and improve accuracy, a full‐colour‐gamut colour prediction system is developed by extending and refining the Friele algorithm, using a full‐colour‐gamut grid colour mixing model as the sample space. First, based on full‐colour‐gamut colour matching requirements, the nine primary‐colour fibres are preferred to construct the grid colour mixing model, which serves as the sample space for the prediction system. Second, the Friele algorithm is enhanced by correlating its model parameters with wavelength and blending concentrations for full‐colour‐gamut matching. Third, 273 blended yarn fabric samples are prepared, and a matching mode is designed to solve the model parameters, resulting in a database of 1674 parameters. Finally, the full‐colour‐gamut colour prediction system is implemented, and its accuracy is validated using verification samples. The validation results demonstrate an average colour difference of 1.1 and an average blending concentration error of 2.56% across the 18 validation samples. It indicates that the constructed system can achieve accurate prediction of colour values and blending formulas for blended yarn within the full‐colour‐gamut range.