## 5 Conclusions  
In this work, an experimental drilling campaign on CFRP/CFRP stacks was conducted by acquiring force, torque, acoustic emission, and vibration signals with a multi-sensor  
Figure 11: Comparison between RNN prediction for architecture (41, 21) with memory window \(w=2\) and experimental tool wear target for experiment 2, with process parameters reported in Table 1. Final root mean squared error (RMSE): \(5.3\cdot 10^{-4}\).  
Figure 12: Comparison between RNN and ANN predictions for architecture (41, 21) and experimental tool wear target for experiment 2 with process parameters reported in Table 1. Final ANN root mean squared error (RMSE): \(6.4\cdot 10^{-4}\). RMSE RNN: \(5.3\cdot 10^{-4}\).  
acquisition system with the aim of developing a smart tool condition monitoring procedure for optimal tool replacement strategy. The acquired signals were processed through an innovative AI-based approach using autoencoders for automatic sensor signal feature extraction and gated recurrent unit (GRU) memory-based recurrent neural networks for tool wear prediction. The results obtained with the different proposed architectures showed that, by using a memory-based neural network such as GRU, the estimation errors were reduced compared to standard feedforward neural networks as well as to alternative machine learning methodologies presented in the literature. In particular, a minimum RMSE of \(3.45\cdot 10^{-4}\) was reached. The results showed that an increment in the architecture complexity, e.g., by adding an additional hidden layer or increasing the number of hidden layer nodes, led to a performance improvement for all datasets. Moreover, a further improvement was also obtained for some experimental tests by incrementing the memory window \(w\) from two to four samples. The main limitation of the presented methodology is related to the need to drill a defined number of holes (equal to the memory window \(w\)) before obtaining the first tool wear prediction, an aspect that has to be taken into account based on the requirements of the application. In conclusion, this work showed the potential of the proposed innovative AI-based approach using autoencoder architectures for automatic feature extraction and GRU memory-based neural networks for tool wear estimation in composite materials drilling aimed at smart tool condition monitoring. By exploiting the combination of different architectures, this approach allows for avoiding intermediate manipulations of the acquired signals, such as manual feature extraction, and has the possibility to make the development of the application simple and fast without reducing the estimation performance.  
Conceptualization, A.C. and G.M.; methodology, A.C. and G.M.; software, G.M.; validation, G.M.; formal analysis, A.C. and G.M.; investigation, A.C. and G.M.; resources, A.C. and L.N.; data curation, A.C.; writing--original draft preparation, A.C. and G.M.; writing--review and editing, A.C. and L.N.; visualization, A.C. and G.M.; supervision, L.N.; project administration, A.C. and L.N.; funding acquisition, L.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, A.C., upon reasonable request.  
The authors declare no conflict of interest.