## 4 Results and Discussion  
The final training results obtained for all the datasets and the diverse architectures are summarized in Table 6 and Figure 10 in terms of root mean square error (RMSE) between the VB values predicted by the different neural network architectures and the experimental VB values. The results show that the proposed methodology allows us to achieve an accurate tool wear prediction for all the datasets, with very low RMSE values always in the range between \(3.45\cdot 10^{-4}\) and \(1.2\cdot 10^{-3}\). In particular, the results show that (i) deeper architectures can be used to better generalize the tool wear behavior and (ii) observing more drilling operations before the tool wear estimation, thus increasing the memory window \(w\), can help increase the estimation performance. The drawback when increasing the memory window \(w\) is related to the need to wait until a number of holes equal to \(w\) are made to obtain the first prediction. Hence, given the excellent tool wear estimation performance achieved even with a small memory window of two samples, it may not be necessary to further increase \(w\). Based on the results obtained in this work, a memory window of two samples seems to be suitable to achieve an accurate tool wear prediction, with performance almost comparable in most cases to the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Architecture** & **Memory Window** & **RMSE** \\ \hline
1 & (21) & 2 & \(1.2\cdot 10^{-3}\) \\
1 & (42, 21) & 2 & \(9.5\cdot 10^{-4}\) \\
1 & (42, 21) & 4 & \(9.5\cdot 10^{-4}\) \\
2 & (21) & 2 & \(6.2\cdot 10^{-4}\) \\
2 & (42, 21) & 2 & \(5.3\cdot 10^{-4}\) \\
2 & (42, 21) & 4 & \(5.1\cdot 10^{-4}\) \\
3 & (21) & 2 & \(5.4\cdot 10^{-4}\) \\
3 & (42, 21) & 2 & \(4.56\cdot 10^{-4}\) \\
3 & (42, 21) & 4 & \(3.45\cdot 10^{-4}\) \\
4 & (21) & 2 & \(1.1\cdot 10^{-3}\) \\
4 & (42, 21) & 2 & \(9.2\cdot 10^{-4}\) \\
4 & (42, 21) & 4 & \(9.1\cdot 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of the different architectures used in this work. The inputs for all architectures are the outputs of the four autoencoders’ (features) and the number of the hole made by the tool. \begin{table}
\begin{tabular}{c c} \hline \hline
**CPU** & Quad-core ARM A57, max frequency 1.43 GHz \\
**GPU** & 128-core NVIDIA Maxwell architecture-based GPU, 512 GFLOPS (FP16 \\
**Memory** & 4 GB 64-bit LPDDR4; 25.6 Gb/s \\
**OS** & Linux for Tegra \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The main characteristic of NVIDIA Jetson nano device. results obtained using a memory window of four samples (see Figure 10). In any case, this choice has to be justified in relation to the application requirements in terms of prediction time and performance. An example of a result obtained with architecture no. 2, i.e., with a memory window of two samples, is reported in Figure 11 for dataset 2 belonging to the experimental drilling tests carried out at 6000 rpm--0.2 mm/rev, for which an RMSE value equal to \(5.3\cdot 10^{-4}\) was obtained. The close proximity between the RNN predicted VB values and the experimental VB values confirms the high accuracy of the RNN prediction that can be effectively utilized for decision making on tool replacement. Comparing the obtained tool wear prediction results for CFRP drilling with the results obtained in [37] using several machine learning algorithms such as Linear Regression (LR), k-Nearest Neighbors (kNN), and Random Forest (RF), where an RMSE of 0.22 was achieved, the methodology proposed in this work shows to reach better results using a more automated data workflow. For a final comparison, the presented methodology was compared to the performance reached using a standard feedforward neural network. In particular, the same architecture, composed of 42 neurons in the first hidden layer and 21 neurons in the second hidden layer, both with a hyperbolic tangent activation function, and with a ReLU activation function in the output layer, was used for both networks.