# Surface Quality of Wood Plastic Composites as Function of Water Exposure
## 3 Results and Discussion
The corresponding values for these specimens with clay were 1.32 and 1.34%, as displayed in Table 2.  
The above findings were also statistically checked through two-way ANOVA analysis and [PERSON]'s post hoc tests, conducted on the average surface roughness (R\({}_{\text{a}}\)) values of the WPC samples. A two-way ANOVA was conducted that examined the effect of Water Exposure Time and WPC Type on the Surface Roughness of the WPC samples. As can be observed in Table 3, there was a statistically significant interaction, with a \(p\)-value of \(<\)0.0001 between the effects of Water Exposure Time and WPC Type on the Surface Roughness of the WPC samples at the alpha level of 0.05. As shown in the same table, a simple main effect analysis showed that there was a significant difference among different levels of \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Panel Type**} & \multicolumn{2}{c}{**Dry Samples**} & \multicolumn{2}{c}{**1-Week Soak**} & \multicolumn{2}{c}{**2-Week Soak**} & \multicolumn{2}{c}{**3-Week Soak**} & \multicolumn{2}{c}{**4-Week Soak**} \\ \cline{2-11}  & **R\({}_{\text{a}}\)** & **R\({}_{\text{z}}\)** & **R\({}_{\text{a}}\)** & **R\({}_{\text{z}}\)** & **R\({}_{\text{a}}\)** & **R\({}_{\text{z}}\)** & **R\({}_{\text{a}}\)** & **R\({}_{\text{z}}\)** & **R\({}_{\text{a}}\)** & **R\({}_{\text{z}}\)** & **R\({}_{\text{a}}\)** & **R\({}_{\text{z}}\)** \\ \hline \multirow{2}{*}{A} & 1.5 & 12.6 & 3.2 & 23.2 & 3.3 & 25.8 & 3.8 & 27.3 & 4.1 & 30.4 \\  & (0.12) & (1.3) & (0.28) & (2.1) & (0.30) & (2.0) & (0.30) & (2.0) & (0.24) & (2.3) \\ \hline \multirow{2}{*}{B} & 1.7 & 13.4 & 3.3 & 24.6 & 3.3 & 26.2 & 3.7 & 29.5 & 4.0 & 32.5 \\  & (0.15) & (1.5) & (0.31) & (2.8) & (0.29) & (2.9) & (0.26) & (3.3) & (0.29) & (2.8) \\ \hline \multirow{2}{*}{C} & 1.6 & 15.2 & 3.1 & 28.9 & 3.4 & 28.2 & 3.8 & 26.2 & 3.9 & 31.9 \\  & (0.18) & (1.2) & (0.38) & (3.5) & (0.33) & (3.4) & (0.39) & (2.9) & (0.30) & (2.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Roughness values of the samples in \(\mu\)m (numbers in parentheses are standard deviation values). Figure 3: Average roughness (R\({}_{\text{a}}\)) values of samples for different exposure times. both Exposure Time and WPC Type, with the same _p_-value of \(<\)0.0001. The two-way ANOVA test results also indicated that a constructed model involving variables of Water Exposure Time, WPC Type, and their interaction term was able to explain 99.3% of the variance in the dependent variable of Surface Roughness (R\({}_{\text{a}}\)). When the partial eta-squared values for WPC Type and Water Exposure Time variables are seen, it can be interpreted that the relative impact of Water Exposure Time was more than nine times as strong as WPC Type. Multiple comparisons of the group means for the WPC Type variable revealed that the difference between Type A and Type B WPC samples was statistically significant, with a _p_-value of \(<\)0.0001 at the alpha level of 0.05. However, no statistically significant difference was observed between the means of Type A and Type C WPC samples (_p_-value = 0.747). The average surface roughness (R\({}_{\text{a}}\)) values of Type A WPC samples were 0.0475 \(\upmu\)m smaller than those of Type B WPC samples. The difference between the average surface roughness values of Type A and Type C samples was 0.0072 \(\upmu\)m, where the surfaces of Type A samples were rougher. Furthermore, when the difference between Type B and Type C WPC samples was investigated, a 0.0547 \(\upmu\)m difference was observed, as can be seen in Table S1. It may be interpreted that increased levels of nano-clay acted as a successful water repellent and ensured a better surface quality under water exposure. As stated above, Water Exposure Time was a more influential factor on surface roughness than WPC Type. Multiple comparison results for 0-4 weeks of exposure yielded statistically significant results at the alpha level of 0.05 for all comparison pairs.