Cutting power and surface quality in sawing kiln-dried, green, and frozen hem-fir wood

Abstract

The lumber manufacturing industry faces challenges due to the cutting of wood with a high variation in moisture content and temperature. In this study, the cutting power and waviness during the circular sawing process of kiln-dried, green, and frozen hem-fir wood were measured and compared under different feed and rotation speeds. An analysis of variance (ANOVA) was performed to evaluate the impact of cutting parameters and the wood condition (kiln-dried, green and frozen), and a decision tree regression model was developed to predict the cutting power and waviness. The ANOVA highlighted that the feed speed had the most impact on cutting power, followed by the wood condition. Similar results were obtained from the decision tree regression (R2 = 0.89). The developed model failed to accurately predict the waviness from the cutting parameters, which emphasized the need for online wood sawing monitoring systems and a deeper understanding of saw blade’s dynamic behavior. The role of moisture content in the cutting power and waviness was nonlinear. The highest cutting power corresponded to sawing frozen wood, while there was no significant difference between sawing dry and green wood. The largest values of waviness were associated with sawing of dry wood, whereas sawing frozen and green wood yielded similar waviness. Sawing at higher rotation speeds yielded an increase in the cutting power but improved the surface quality by lowering the waviness.