Abstract:

At present, most of color spinning enterprises still rely on experienced color matching persons for color matching, and some problems such as low color matching efficiency and poor accuracy still exist in the production. In order to solve these peoblems, back propogation (BP) neural network method was proposed to predict black and white fiber color matching in comparison with the prediction results using the Datacolor MATCH system simulation method and color mixing based model Kubelka-Munk two-constant theory. The above-mentioned three methods were all determined to be effective in predicting color mixing of black fiber and white fiber in gray spun yarns. The relative errors were controlled within 7.36%, and the color differnces between formula and standerd samples were less than1. It is found that the matching method based on BP neural network shows the optimal applicability, and the relative error is below 3.08%. 

Key words: gray spun yarn, color matching method, model, Kubelka-Munk two-constant theory