Wood Classification Study based on Thermal Physical Parameters with Intelligent Method of Artificial Neural Networks

In this study, 65 kinds of wood samples were classified by using artificial neural networks based on the measured value of wood thermal physical parameters. First, the thermal conductivities and the thermal diffusion coefficients of the wood samples were measured. The transient temperature rise curve of wood samples during the test process was recorded, and the characteristic values of the transient temperature rise curve were extracted by logarithmic curve fitting. The emissivity spectrum representing the thermal physical properties of wood surface was measured, and the characteristic spectral data were selected according to the principal component analysis. An artificial neural network model was established based on the extracted feature values and characteristic spectral data to classify the wood species. The experimental results showed that the comprehensive correct classification rate of the proposed wood classification method was 99.85%. In addition, the proposed wood classification method was compared with a wood classification method based on laser induced breakdown spectrum and near infrared spectrum, which indicates the feasibility of wood classification based on the values of wood thermal physical properties.

Automated summary

In this study, 65 types of wood samples were classified using artificial neural networks based on measured values of wood thermal physical parameters. The study established a classification model by extracting feature values and characteristic spectral data and achieved a high correct classification rate of 99.85%. The feasibility of wood classification based on wood thermal physical properties was also demonstrated through comparison with other classification methods.