Characterization, optimization, and acoustic emission monitoring of airborne dust emission during wood sawing

This study investigates the airborne dust generation during the wood sawing process. It also develops an intelligent framework for airborne dust monitoring using acoustic emission (AE) sensor during the sawing process of green Douglas-fir wood. It uses AE signals and artificial neural networks to predict the dust emission at different cutting conditions. Also, the statistical effect of cutting parameters on the mass concentration of airborne dust was analyzed. The study investigates sustainable wood manufacturing by considering the effect of airborne dust emission alongside the power consumption and product quality index. It was observed that the mass concentration of the airborne dust is linearly increased with the rotation speed but inversely proportional to the depth of cut, feed speed, and the average chip thickness. The role of the depth of cut and uncut average chip thickness in the generated dust is complex and is more significant when having a small depth of cut. The generated dust emission at different conditions was then compared with its corresponding cutting power and product quality index. The results indicated that developing an AE monitoring system is a reliable method of predicting the amount of inhalable dust that typically measured by an airborne dust detector. This could enable manufacturers to identify the source of high dust emission when different machines are simultaneously operated and adjust their respective cutting parameters to lower the generated airborne dust. A multi-objective optimization should be considered to identify the condition resulting in a low dust emission while having a high product quality and energy-efficient process.