MODELLING OF BIRCH WOOD (BETULA SPP.) SURFACE ROUGHNESS DEPENDING ON LASER PROCESSING PARAMETERS WITH RESPONSE SURFACE METHODOLOGY

In this study, the effect of laser engraving parameters on birch wood surface roughness was investigated with response surface methodology (RSM) and a mathematical model was developed. Laser power (W%), speed (mm/min) and space (mm) were selected as independent variables. Ra, Rq and Rz surface roughness indices were measured as response variables. In the study, 20 experimental conditions were conducted with 3-factor and 3-level experimental designs using central composite design (CCD). Laser engraving power and space were determined as the most effective factors on surface roughness. Regression models were developed for each surface roughness index and the measurements were estimated. In the model created for Ra surface roughness, R² = 97.0% and the average error was found to be 4.9%, for Rq, R² = 96.8% and the average error was found to be 4.5%, for Rz, R² = 96.6% and the average error was found to be 4.0%. It is understood that the surface roughness values will be predicted with high accuracy with the created model.

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Publication Details

Journal
Wood Research
Published
2025-06-06
DOI
https://doi.org/10.37763/wr.1336-4561/70.2.286298
Primary Topic
Surface Roughness and Optical Measurements
Type
article
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article

MODELLING OF BIRCH WOOD (BETULA SPP.) SURFACE ROUGHNESS DEPENDING ON LASER PROCESSING PARAMETERS WITH RESPONSE SURFACE METHODOLOGY

Çağatay Ersin
Wood Research
Surface Roughness and Optical Measurements
article

MODELLING OF BIRCH WOOD (BETULA SPP.) SURFACE ROUGHNESS DEPENDING ON LASER PROCESSING PARAMETERS WITH RESPONSE SURFACE METHODOLOGY

Çağatay Ersin
article en

Abstract

In this study, the effect of laser engraving parameters on birch wood surface roughness was investigated with response surface methodology (RSM) and a mathematical model was developed. Laser power (W%), speed (mm/min) and space (mm) were selected as independent variables. Ra, Rq and Rz surface roughness indices were measured as response variables. In the study, 20 experimental conditions were conducted with 3-factor and 3-level experimental designs using central composite design (CCD). Laser engraving power and space were determined as the most effective factors on surface roughness. Regression models were developed for each surface roughness index and the measurements were estimated. In the model created for Ra surface roughness, R² = 97.0% and the average error was found to be 4.9%, for Rq, R² = 96.8% and the average error was found to be 4.5%, for Rz, R² = 96.6% and the average error was found to be 4.0%. It is understood that the surface roughness values will be predicted with high accuracy with the created model.

Wood ResearchVol. 70(2)
Openalex Percentile: Top 86%
Surface Roughness and Optical Measurements
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