## 3 Results and Discussion
### Surface Roughness
This much higher roughness recorded in transverse surfaces (when measured parallel to the wood grain) could be explained by the fact that chestnut is a ring-porous wood species and when the measurement of roughness is conducted in parallel orientation to the wood grain, there is a high potential for the measurement to be implemented alongside the earlywood area, which consists of cross-sectional cut vessels (of large diameter) [8] that would definitely increase the roughness of the surface. Figure 9: Estimated marginal means plot of surface roughness index of _Ra_ (\(\upmu\)m), recorded in measurements that were conducted in different orientations (vertically and parallel to the wood grain). After the examination of all possible pairs of factors in terms of combination and interaction, we decided to apply a \"decomposition\" of the statistical analysis, abstracting the factor \"Area\" (heartwood-sapwood) due to the fact that it did not demonstrate a statistically significant impact. Moreover, the results of the remainder of the variable combinations revealed that the 67.6% of the variability of surface roughness (_Ra_) is being influenced by the factor of measurement \"Direction\" (vertically/parallel) and only 7.8% by the factor \"Planes\" (transverse, radial, tangential), proving that the \"Planes\" variable, although inducing statistically significant differences in roughness values, has quite a low impact on the surface roughness (Figure 10). Additionally, regarding the transverse plane measurements, the _Ra_ variability was found to be influenced by the factor measurement \"Direction\" (vertically/parallel) by 20.9% (Table 2). In the radial plane measurements, the impact of the measurement \"Direction\" factor on _Ra_ was found to be 48%, while in tangential measurements, 50.7% of the _Ra_ variability was affected by the \"Direction\" factor. The effect of the \"Planes\" factor (transverse, radial, tangential) on the _Ra_ values with regard to the vertical measurements was very low (1.1%), while for the parallel measurements, it was 18.5%. \begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Surface Planes** & **Sum of Squares** & **df** & **Mean Square** & **F** & **Sig.** & **Partial Eta Squared** \\ \hline \multirow{2}{*}{Transverse} & Contrast & 1383.516 & 1 & 1383.516 & 395.408 & \textless{}0.001 & 0.209 \\ \cline{2-7}  & Error & 5227.442 & 1494 & 3.499 & & & \\ \hline \multirow{2}{*}{Radial} & Contrast & 4972.470 & 1 & 4972.470 & 1421.129 & \textless{}0.001 & 0.488 \\ \cline{2-7}  & Error & 5227.442 & 1494 & 3.499 & & & \\ \hline \multirow{2}{*}{Tangential} & Contrast & 5373.363 & 1 & 5373.363 & 1535.704 & \textless{}0.001 & 0.507 \\ \cline{2-7}  & Error & 5227.442 & 1494 & 3.499 & & & \\ \hline \hline \end{tabular} df: degrees of freedom, F: F-value is the ratio of between-group and within-group variation, Sig.: significance.  
\end{table} 
Table 2: Analysis of variance output—Univariate tests of _Ra_ (dependent variable). Figure 10: Clustering line chart of surface roughness index (_Ra_, \(\upmu\)m) depending on the different planes (transverse, radial, tangential), area (sapwood, heartwood), and direction of measurement (vertically, parallel).