# Prediction Models for the Milling of Heat-Treated Beech Wood Based on the Consumption of Energy
## 3 Results and Discussion  
The descriptive statistics of cutting power are presented in Table 6. The cutting power values were calculated according to Equation (2), i.e., as the difference between the recorded power when the milling machine was running and idling. Aspects of the statistical significance of the effects of technical/technological parameters on cutting power were evaluated by ANOVA. At the same time, the cutting power values were calculated as the difference between milling and idling power consumption for every single milling process. The results are shown in Figure 6, wherein vertical bars indicate a confidence interval of 95%. The cutting speed had the most significant impact on cutting power, as determined by the F-test (F-test: 127.04), followed by the rake angle (69.59) and material (F-test: 6.97). The feed speed had the lowest impact on the cutting input (F-test: 2.32). Similar correlations were also found by the authors of [27]. Figure 5: Connection of measuring apparatus: 1—frequency converter and sine filter; 2—asynchronous motor; 3—milling machine; 4—computer (laptop); L1, L2, L3—phase wires; U, V, W—motor wires. From the curves in Figure 6, it was confirmed that increasing the cutting speed increases the power required to remove the material with the cutting tool because cutting power is a product of the elementary cutting force and cutting speed [28]. The same conclusions are confirmed by both older and more recent results of milling wood and wood-based materials, e.g., in [29; 30; 31]. From the point of view of the material, the highest values of cutting power are obtained when milling natural wood and the lowest when milling wood treated at the highest temperature. This dependence is related to the density of the wood and the chemical processes that change it during hydrothermal treatment, which is also confirmed, for example, in [32; 33]. However, a slight decrease in density was detected at a temperature of 105 \({}^{\circ}\)C.  
The measured data were tested for the probability of similarity by [PERSON]'s post hoc test. Here, the effects of changing the cutting speed, feed speed, and rake angle were statistically significant. This means that the probability of the similarity of data sets for individual groups of variables was less than 5%. For the heat-treatment temperature, the similarity probabilities of the data sets were also below 5%, except for the data obtained at temperatures of 105 and 125 \({}^{\circ}\)C, where it was 33.2%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Value** & \begin{tabular}{c} **Average** \\ **P (W)** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \begin{tabular}{c} **Standard** \\ **Error** \\ \end{tabular} & \
\begin{tabular}{c} **-95\%** \\ \end{tabular} &
\begin{tabular}{c} **+95\%** \\ \end{tabular} \\ \hline \(v_{\text{f}}\) & 6 & 83.57 & 42.68 & 2.87 & 77.91 & 89.23 \\ (m\(\cdot\)min\({}^{-1}\)) & 10 & 89.98 & 50.48 & 3.68 & 82.72 & 97.24 \\  & 15 & 99.59 & 53.90 & 4.46 & 90.78 & 108.41 \\ \hline \multirow{3}{*}{\(\gamma\) (\({}^{\circ}\))} & 20 & 54.93 & 25.95 & 2.00 & 50.98 & 58.89 \\  & 25 & 100.22 & 45.90 & 3.31 & 93.68 & 106.75 \\  & 30 & 110.03 & 50.84 & 3.64 & 102.85 & 117.21 \\ \hline \multirow{3}{*}{\(v_{\text{c}}\) (m\(\cdot\)s\({}^{-1}\))} & 20 & 54.07 & 23.69 & 1.66 & 50.79 & 57.36 \\  & 40 & 95.24 & 36.27 & 2.74 & 89.83 & 100.65 \\  & 60 & 125.48 & 52.61 & 3.94 & 117.70 & 133.26 \\ \hline \multirow{3}{*}{\(T\) (\({}^{\circ}\)C)} & 105 & 88.14 & 46.30 & 3.88 & 80.46 & 95.82 \\  & 125 & 91.14 & 49.77 & 4.20 & 82.83 & 99.46 \\ \cline{1-1}  & 135 & 79.29 & 45.61 & 3.91 & 71.56 & 87.03 \\ \cline{1-1}  & N & 101.21 & 51.47 & 4.39 & 92.52 & 109.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Descriptive statistics of cutting power. Figure 6: Analysis of variance of selected independent variables. The creation and testing of mathematical models were realised using STATISTICA 12 software with the data mining tool.