Abstract
To improve the accuracy and practicality of the intelligent color-matching application of wood dyeing technology, Fraxinus mandshurica veneer was selected as the dyeing material. First, based on the Friele model and Stearns–Noechel model, the model parameters were cyclically assigned to calculate the optimal fixed parameter values and predictions. Then, particle swarm algorithm was used to optimize two algorithm models, the obtained reflectance curve was fit, and the color differences were calculated according to the human eye-based CIEDE2000 color difference evaluation standard formula. Last, the two formulas to predict the color difference and spectral reflectance were compared. First, the two optimization algorithms were compared according to the size of the fitted color difference value, and then, the most accurate optimization algorithm was selected. When the model parameters were fixed, the average fitted color difference was 0.8202. After optimizing the Friele model, the average fitted color difference was 0.7287, and after optimizing the Stearns–Noechel model, the average fitted color difference was 0.6482. It was concluded that the improved Stearns–Noechel model based on particle swarm method was more accurate than the Friele model for wood color matching.
Highlights
To achieve efficient use of plantation forests, the computer-assisted dyeing and color-matching technologies are used to improve inferior materials and imitate valuable materials (Guan 2011; Guan et al 2010)
It was concluded that the improved Stearns–Noechel model based on particle swarm method was more accurate than the Friele model for wood color matching
Using the method based on Bayesian normalization algorithm and Levenberg–Marquardt (LM) algorithm to improve the traditional BP neural network has improved the accuracy of the simulation concentration and the generalization of the network (Nie et al 2008), but the network convergence speed has not been significantly improved, and the training sample size is
Summary
To achieve efficient use of plantation forests, the computer-assisted dyeing and color-matching technologies are used to improve inferior materials and imitate valuable materials (Guan 2011; Guan et al 2010). Based on the application of the improved RBF neural network in wood dyeing and color-matching technology, the generalization ability of the network is gradually improved to a certain extent (Guan et al 2016). The convergence speed, to a certain extent, is superior to conventional neural network learning algorithms. The Stearns–Noechel algorithm is used to establish a prediction model for each sample, and the model parameters are modified. Using this model can make the prediction of color measurement more accurate, greatly reduce the color difference of color matching, and improve the color-matching effect (Wang 2017).
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
