## 3 Results and Discussions
### ANN Model Validation  
The new ANN model developed was validated in an experimental study and by manufacturing a CFRP part in an industrial application. To validate the model, the Simulink platform 2023 was used. In Figure 7, the Simulink diagram designed for this test is shown. The input block (the constant block--x1) was configured using the validation input data. The neural network block was then loaded with the trained network parameters. The output block (y1) was set up to capture the model's predictions.  
Figure 6: The morphology of the machined surfaces: (**a**) the delaminated specimen and (**b**) the specimen with free delamination.  
After executing the simulation in Simulink, the predicted outputs for the validation inputs were generated. These outputs were then compared with actual measurements from the validation dataset. The AWJ equipment used in this validation was the same industrial equipment used in the primary experiments, the Tecnocut Milestone 1740 machine.  
The ANN model's predictions were compared with the values obtained during the experimental tests. Table 4 presents the process parameters used and the experimental results.  
A paired-sample t-test was conducted to compare the predicted values and the measured test values. There was no significant difference in the scores for the predicted values (M = 12.56, SD = 9.77) and measured values (M = 12.08, SD = 9.82); (t (9) = 0.496), (\(p\) = 0.632). A two-tailed test was used with a significance level of \(\alpha\) = 0.05. This confirmed the reliability of the ANN in predicting delamination extent values.  
The proposed model was applied to identify optimal process parameters for machining 50 CFRP industrial parts with a thickness of 6 mm with AWJ. Both the external contours and two slots were machined with AWJ, as illustrated in Figure 8a.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Run** & **P** & **SOD** & **ASS** & **t** & **Predicted D\({}_{\text{ext}}\)** & **Measured D\({}_{\text{ext}}\)** \\ \hline  & **MPa** & **mm** & **On/Off** & **mm** & **mm** & **mm** \\ \hline
1 & 350 & 8 & Off & 2 & 24.1 & 25 \\
2 & 100 & 8 & Off & 2 & 3.6 & 8 \\
3 & 350 & 8 & On & 2 & 21 & 17.8 \\
4 & 100 & 8 & On & 2 & 1.9 & 0 \\
5 & 350 & 1 & Off & 5 & 21.9 & 18 \\
6 & 100 & 1 & Off & 5 & 4.1 & 0 \\
7 & 350 & 5 & Off & 5 & 23 & 25 \\
8 & 100 & 5 & Off & 5 & 8.1 & 11.5 \\
9 & 350 & 0.5 & On & 5 & 17.5 & 15.5 \\
10 & 100 & 0.5 & On & 5 & 0.38 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental results.  
Figure 7: The Simulink diagram for ANN validation.  
In the first setup, the process parameter selection was made on the basis of the recommendations provided by the manufacturer of the AWJ Tecocut equipment for piercing general brittle material. The process parameters for AWJ piercing were P = 150 MPa (low pressure for piercing), SOD = 1 mm, ma = 0.45 kg/min, t = 6 mm, and abrasive start synchronization OFF; for AWJ cutting, the process parameters were P = 350 MPa (high pressure for cutting) and V = 300 mm/min. The part was processed in 1.8 min, resulting in significant fiber layer delamination, as shown in Figure 8b.  
On the basis of the optimization made with the new ANN model, the following process parameters were used for piercing: P = 100 MPa, ma = 0.45 kg/min, SOD = 0.5 mm, and abrasive start synchronization ON. These parameters resulted in a predicted delamination extent (D\({}_{\text{ext}}\)) of 0 mm. For cutting, the parameters were P = 350 MPa, ma = 0.45 kg/min, SOD = 0.5 mm, and V = 300 mm/min. Figure 8c shows the part produced with the proposed piercing method. This part was processed in the same machining time with no delamination and met the required quality characteristics.  
The optimized parameters ensure AWJ piercing without delamination, validating the ANN model's industrial applicability.