Intelligent prediction model for fabric dyeing formulations based on GWO-CNN
YANG Hongying
YANG Yubin
ZHANG Ge
LIU Yadong
ZHAO Shilong
ZHANG Jingjing
XIE Wanzi
Abstract：To improve the intelligent prediction of fabric dyeing formulas,this paper adopts the re-flection spectrum color matching method,taking the spectral reflectance R(λ)of dyed fabrics as input data and the dye formulas as output data,and constructs intelligent color matching models based on the deep learning model Convolutional Neural Network(CNN)and the Convolutional Neural Network optimized by the Grey Wolf Optimizer(GWO-CNN)respectively.To comprehensively evaluate the models' performance,the coefficient of determination(R2)and the mean absolute error(MAE)be-tween predicted and actual dye formulas were selected as key metrics.The training and validation re-sults demonstrate that the GWO-CNN model achieved an R2 value of 0.991,outperforming the stan-dalone CNN model's 0.964.In predicting the concentrations of three dyes,the GWO-CNN model ex-hibited MAEs of 0.006,0.004,and 0.004 respectively,all lower than the CNN model's 0.007,0.005,and 0.004.The research results demonstrate that the application of the GWO-CNN color matching model can effectively enhance the prediction of fabric dyeing formulas.
Keywords：color matching modeldeep learningconvolutional neural networkgrey wolf optimi-zerspectral reflectance
Publication Date：2025-08-25
Online Publishing Date：2025-09-25(First online date of this platform, not the publication date of the document)
Pages：6( 26-31 )