This research is devoted to solving the bottleneck problem of spinning and color prediction of rotor spun melange yarn within the full color gamut range. Firstly, a full color gamut grid color mixture model with hue regulation range of 0–360°, saturation regulation range of 0-1 and lightness regulation range of 0-1 was constructed based on the preferred seven primary-color fibers through grid color mixing. Secondly, in the full color gamut grid color mixture model, 55 representative grid points were planned and corresponding melange yarns and fabrics were prepared based on grid point parameters, which served as the training set, testing set, and validation set for constructing neural network prediction models. Thirdly, based on the demand for color prediction of melange yarns, a color prediction model and a blending ratio prediction model with six sub-models are constructed by taking the reflectivity of the melange yarn fabric and the blending ratio of the primary-color fibers as input and output values. Finally, six prediction samples were produced to verify the predictive performance of the neural network prediction model. The results show that the average color difference between the predicted and measured colors is 1.06, and the average root mean square error value between the predicted and actual blending ratios is 0.48%. It indicates that the neural network prediction model based on the grid mixing of seven primary-color fibers can adapt to the prediction of the mixing color and blending ratio of primary-color fibers in the full color gamut. Due to the construction of six sub-models based on local hue regions, the prediction accuracy of the neural network prediction model has been greatly improved.
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