Research on a colour solid built by gridded colour mixing of nine primary-coloured fibres and its neural network colour prediction approach

Abstract

According to the demand for colour prediction for coloured yarn, two adjacent colours chosen from red (R), yellow (Y), green (G), cyan (C), blue (B) and magenta (M) fibres were combined with fibres of dark grey (O¹), medium grey (O²) and light grey (O³), respectively, and then ternary coupling-superposition mixing was performed to acquire a colour solid consisting of three lightnesses, 18 colour mixing units and 18 × (m + 1) × n grid points. An integrated colour mixing with 20% hue gradient and 33.33% saturation gradient was performed to achieve a colour solid containing 360 grid points, then using it as the sample space for the colour prediction model. A total of 360 typical samples were established by the grid points, 213 yarns and fabrics were prepared by the typical sample parameters, and the corresponding reflectance was accessed by a spectrophotometer. Neural network models for predicting reflectance by mixing ratios as well as forecasting mixing ratios by reflectance, were established. The 12 non-grid point parameters were chosen to prepare corresponding yarns and fabrics, and the corresponding reflectance was measured. The predicted and measured values of the neural network model were compared to verify its predictive ability and generalisability. The results showed that when predicting the colour by the mixing ratios, the colour difference between the predicted and measured samples ranged from 1.5 to 3.4, with an average of 2.4; and when forecasting the mixing ratios by the colour, the colour difference ranged from 0.8 to 5.6, with an average of 2.4.