Enhanced Kubelka-Munk theory with Stearns-Noechel integration for precision color prediction in wide-gamut digitally controlled rotor spinning

• Novel Hybrid Model: Developed an enhanced Kubelka-Munk (K-M) model by integrating the Stearns-Noechel (S-N) theory to significantly improve color prediction accuracy. • Wide-Gamut Digital Spinning: Created a system for real-time, wide-gamut color control in yarn production using a three-channel numerical control (NC) rotor spinner. • Proven Accuracy Gains: Reduced the average color prediction error (ΔE) by 36% (from 2.31 to 1.47), with dramatic improvements for previously problematic color blends. • Flexible Digital Workflow: Established a practical "color-to-recipe" and "recipe-to-color" digital framework, enabling unprecedented production flexibility. • 5.Sustainable Manufacturing: Offers a viable, eco-friendly alternative to conventional dyeing, reducing water and chemical consumption in textile coloration. This research introduces a comprehensive Kubelka-Munk (K-M) double-constant theory, enhanced by Stearns-Noechel (S-N) model integration, for precise color prediction in wide-gamut rotor-spun yarns. To overcome limitations in traditional color-blended yarn production, we developed an innovative grid-based color mixing model covering a wide portion of the visible color space, utilizing a ternary coupling system (two chromatic fibers, one gray) across three mixing regions for precise hue, saturation, and lightness control. Implemented in a three-channel numerical control (NC) rotor spinning system, this approach facilitates real-time 'first-color, then-spin' yarn production. The core methodological innovation—the enhanced K-M/S-N theory—specifically addresses prediction inaccuracies when calculated reflectance is below actual measurements. Systematic testing on 30 diverse yarn samples confirmed significant accuracy improvements, reducing the average color difference (Δ E ) from 2.31 to 1.47, with notable gains for previously problematic samples. This study offers a robust theoretical and practical methodology for precise, efficient, and sustainable production of wide-gamut color-spun yarns, advancing digitally-integrated textile manufacturing.