Characterization of wood dust emission according to some wood species in 3D machining applied with CNC machine

Abstract The use of CNC machines for woodworking is becoming increasingly widespread due to advancing technology. In this study, while 3D processing is performed on wood materials using a CNC machine, the aim is to determine wood dust emissions (PM2.5, PM10) according to processing parameters and wood species. Yellow pine, spruce, chestnut, sapelli, and beech are wood species frequently preferred in furniture businesses. Utilizing a CNC machine equipped with 2 mm and 10 mm diameter cutting tools, the wooden material was machined at a spindle speed of 18,000 RPM, a plunge rate of 4000 mm/min, and a cutting speed of 8000 mm/min. For the study, a 3D-modeled motif with curved lines was selected and applied to wooden prisms measuring 30 cm × 30 cm × 30 mm (width, length, thickness). In conclusion, Yellow pine, with its high hardness and dense structure, generates significantly more dust than spruce during CNC machining. This result reveals that different wood species exhibit different levels of environmental impact during CNC processing. This underscores the urgent need for appropriate air filtration and emission control systems, particularly for woods that produce high emissions. Fine cutting edges effectively reduce PM2.5 and PM10 concentrations, enhancing workplace air quality. By reducing exposure to wood dust, which is harmful to human health, this study plays a crucial role in addressing this pressing issue and allows for the implementation of suitable arrangements with minimal chip removal for CNC parameters.

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Publication Details

Journal
European Journal of Wood and Wood Products
Published
2025-02-18
DOI
https://doi.org/10.1007/s00107-025-02222-2
Citations
5
Primary Topic
Advanced machining processes and optimization
Type
article
Field-Weighted Citation Impact
3.57
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article

Characterization of wood dust emission according to some wood species in 3D machining applied with CNC machine

Kazım Onur Demirarslan, Taner Taşdemir, Evren Osman Çakıroğlu
5 citations
European Journal of Wood and Wood Products
Advanced machining processes and optimization
3.57
article

Characterization of wood dust emission according to some wood species in 3D machining applied with CNC machine

Kazım Onur Demirarslan, Taner Taşdemir, Evren Osman Çakıroğlu
article en
5 citations

Abstract

Abstract The use of CNC machines for woodworking is becoming increasingly widespread due to advancing technology. In this study, while 3D processing is performed on wood materials using a CNC machine, the aim is to determine wood dust emissions (PM2.5, PM10) according to processing parameters and wood species. Yellow pine, spruce, chestnut, sapelli, and beech are wood species frequently preferred in furniture businesses. Utilizing a CNC machine equipped with 2 mm and 10 mm diameter cutting tools, the wooden material was machined at a spindle speed of 18,000 RPM, a plunge rate of 4000 mm/min, and a cutting speed of 8000 mm/min. For the study, a 3D-modeled motif with curved lines was selected and applied to wooden prisms measuring 30 cm × 30 cm × 30 mm (width, length, thickness). In conclusion, Yellow pine, with its high hardness and dense structure, generates significantly more dust than spruce during CNC machining. This result reveals that different wood species exhibit different levels of environmental impact during CNC processing. This underscores the urgent need for appropriate air filtration and emission control systems, particularly for woods that produce high emissions. Fine cutting edges effectively reduce PM2.5 and PM10 concentrations, enhancing workplace air quality. By reducing exposure to wood dust, which is harmful to human health, this study plays a crucial role in addressing this pressing issue and allows for the implementation of suitable arrangements with minimal chip removal for CNC parameters.

European Journal of Wood and Wood ProductsVol. 83(2)
Artvin Coruh University (TR)
Openalex Percentile: Top 8%
Advanced machining processes and optimization
3.57
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