LAUSR

The elastic strain of wood reflects the nature (stretching or compression) and the magnitude of the drying stress at that time during the conventional drying process. The accurate prediction of strain is important to optimize the drying process and to improve drying speed and quality. In this work, the elastic strain was measured in real time, and moisture content was measured by periodic weighing during the drying process. Using these data, the GM (1,1) grey prediction model was used to predict moisture content in adjacent periods in the future. Based on the moisture content predicted by GM (1,1), a BP neural network was constructed to predict the development trend of elastic strain in the surface layer and core layer. The prediction results of the GM-BP combination model showed that the fitting error range of the prediction of the surface layer elastic strain was [-5×10-3~5×10-3], with a mean square error (MSE) of 2.31×10-7. The elastic strain of the core layer was [-2×10-3~2×10-3], and the MSE was 3.86×10-8. Thus, the GM-BP model achieved high accuracy for predicting the development trend of elastic strain. It can provide a new method and innovative thinking for the optimization and control of wood drying process.