## 3 Results and Discussion  
### ANN Modeling  
In Table 4, various architectures of designed ANN models that are able to predict the thrust force and drilling torque during drilling of plywood can be observed. Based on the coefficient of correlation (R), the following findings were revealed. The most important input that affects the performance of a designed ANN model to predict the thrust force (\(Y_{1}\)) is the drill type (\(X_{1}\)), followed by the tooth bite (\(X_{2}\)) and drill tip angle (\(X_{1}\)). In the case of the model developed to predict the drilling torque (\(Y_{2}\)), the tooth bite has a greater influence than the other two analyzed variables, namely, drill tip angle (\(X_{1}\)) and drill type (\(X_{3}\)). The drill type has a more important role in the performance of model than the drill tip angle (\(X_{1}\)). However, the best performance of both designed ANN models is obtained for the case when all three input variables were considered in the model. The coefficient of correlation was equal to 0.98 for both ANN models.  
The performance indicators of ANN developed models are presented in Table 5. As can be observed, the developed models have a good accuracy, namely, a high R or R\({}^{2}\), and a low RMSE or MAPE. This affirmation is also supported by Figures 6 and 7.  
The architecture of designed ANN models is different in the hidden layer both in the case of thrust force and drilling torque when it is compared with the architecture developed to predict the same outputs. However, in the case of prelaminated wood particle panels [16], the number of neurons in the hidden layer was equal to four in the case of the ANN model developed to predict the thrust force and nine for the model designed to predict the drilling torque. Since the same inputs and outputs were analyzed, the difference in the number of neurons could be attributed to the analyzed material in this work, namely, plywood, which has a different structure than prelaminated wood particle panels. Thus, it could be stated that the type of material influences the architecture and performance of artificial neural networks. Consequently, in a further study, the type of material should be included as one of the input of neural networks among other factors.  
To summarize, both developed ANN models have a high coefficient of determination (R\({}^{2}\approx\) 0.97). Consequently, the designed models explain at least 97% of experimental data used during the modeling process. By comparing the value of coefficient of determination with the results from other studies, one could observed that the value is close to the value obtained by [PERSON] et al. [26] to predict power consumption during wood planning, [PERSON] and [PERSON] [21] to predict noise emissions during wood machining, [PERSON] et al. [27] to predict surface roughness and power consumption during the abrasive machining of wood, and [PERSON] et al. [16] to predict the thrust force and drilling torque during the drilling of prelaminated wood particle panels.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Performance Indicator** & **ANN Model for Thrust Force** & **ANN Model for Drilling Torque** \\ \hline R & 0.986 & 0.988 \\ R\({}^{2}\) & 0.972 & 0.976 \\ RMSE & 16.59 & 0.10 \\ MAPE & 6.80 & 7.39 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The performance indicators of ANN models designed to predict the thrust force and drilling torque.  
Figure 6: The graphical comparison between the experimental and predicted values of thrust force: linechart (**a**) and scatter plot (**b**).  
Figure 7: The graphical comparison between the experimental and predicted values of drilling torque: line chart (**a**) and scatter plot (**b**).