@article{Li_He_Xu_Wang_2022, title={Prediction of surface roughness of CO2 laser modified poplar wood via response surface methodology}, volume={24}, url={https://revistas.ubiobio.cl/index.php/MCT/article/view/5397}, DOI={10.4067/s0718-221x2022000100442}, abstractNote={<p>Due to the advantages of short treatment period, no wastewater and oil produced, the CO<sub>2</sub> laser is applied as an environment friendly thermal treatment for wood materials to improve the wood properties, such as appearance, color and wettability, <em>etc.</em> However, the morphological features of treated wood surface are also changed, which have negative effects on wooden product properties. To reveal the change tendency of surface roughness during laser irradiation, the common indexes of average roughness (Ra) and mean peak-to-valley height (Rz) were chosen to evaluate the surface roughness. The response surface methodology was selected to arrange the experiments and analyze the influences of laser parameters on surface roughness. The results showed that the poplar wood got rougher with the increased laser power, but the surface roughness decreased with increased feed speed and path spacing, due to the total heat absorption varied under different combination of laser parameters. The ANOVA results showed that the selected quadratic models for Ra<sub>∥</sub>, Rz<sub>∥</sub>, Ra<sub>⊥ </sub>and Rz<sub>⊥</sub>were significant due to the values of probability value (“Prob>F”) less than 0,05. In this case, all the input laser parameters were also the significant model terms for variation of surface roughness. The values of correlation coefficient were very close to 1, which meant the selected quadratic models could give accurate prediction of surface roughness for laser treated wood. Therefore, it is of great significance to predict the surface roughness of the modified wood surface scientifically and to guide the selection of reasonable modification process parameters.</p>}, journal={Maderas-Cienc Tecnol}, author={Li, Rongrong and He, Chujun and Xu, Wei and Wang, Xiaodong}, year={2022}, month={Mar.}, pages={1–12} }