Predicting color change in wood during heat treatment using an artificial neural network model

Abstract

Understanding and mastering the color change of wood during heat treatment is essential in the wood working industry because it saves time and reduces energy costs. An artificial neural network (ANN) was employed in this study to establish the relationship between the process parameters of heat treatment and the color change of wood. Three important parameters: temperature (180 °C, 190 °C, 200 °C, 210 °C, and 220 °C), treatment time (2 h, 4 h, 6 h, and 8 h), and wood species (larch and poplar) were considered as inputs to the neural network. There were four neurons in the hidden layer that were used, and an output layer as wood color. According to the results, the mean absolute percentage errors were determined as 0.53%, 0.65%, and 0.31% in the prediction of color change color (ΔE) values for training, validation, and testing data sets, respectively. Determination coefficients (R2) greater than 0.99 were obtained for all data sets with the proposed ANN models. These results showed that ANN models can be used successfully for predicting the color changes in wood during heat treatment.