Research on the Construction of a Full-Color-Gamut Color Spinning Method and Neural Network Color Prediction System

This paper aims to develop a comprehensive technology for full-color-gamut spinning to enable the precise design and production of blended yarns. A grid-based color-mixing model with six regions is built as a cylindrical color model by mixing eight primary color fibers. Using a three-element synergistic regulation mechanism comprising a numerically controlled rotor-spun system, a color-spinning method is created that integrates the full-color-gamut grid-based color-mixing model. Consequently, 273 blended yarn fabrics are produced. Leveraging the regional variations in blending primary color fibers, a neural network color-prediction system is designed. It is trained on reflectance and blending-ratio data from 255 samples and validated with 18 samples across different color-mixing regions. The results show a mean color difference prediction of 0.909 and an average blending ratio error of 1.76% for the 18 samples. This indicates that the color-prediction system can accurately predict the colors of blended yarns within the full-color-gamut range, providing theoretical support for yarn production.