## 4 Conclusions  
In this study, an artificial neural network model was used to identify the optimal combination of process parameters for eliminating delamination in composite materials during AWJ piercing. The model was trained using backpropagation with MATLAB's neural network toolbox and validated through both experimental studies and industrial applications.  
The developed ANN model accurately predicts the delamination extent (D\({}_{\text{ext}}\)) during AWJ piercing. The high correlation between predicted and actual values (training R = 0.97843, validation R = 0.97572, testing R = 0.96891) shows the model's reliability and effectiveness in optimizing process parameters. The validation tests showed that there are no statistically significant differences between test results and predictions, qualifying the model as a reliable basis for process optimization.  
The ANN model can help in identifying optimal process parameters (water pressure, standoff distance, and abrasive start synchronization) and ensure piercing without delamination. This optimization helps to maintain the structural integrity and performance of CFRP components in industrial applications.  
The Simulink platform was effectively utilized to validate the ANN model. The validation process included configuring input and output blocks and ensuring the neural network block was loaded with trained parameters. This platform can be easily used for developing real-time monitoring and control systems for industrial implementation.  
Figure 8: The CFRP cut for the validation of the proposed method.  
The ANN model was successfully applied to the machining of 50 CFRP industrial parts without delamination. This real-world application confirmed the model's robustness and practical utility.  
Future research should focus on incorporating additional process parameters, such as abrasive characteristics, cutting head dimensions, and material characteristics, to enhance the ANN model's generalizability. Also, with a larger dataset, the ANN should be able to identify more intricate patterns in the data and make better predictions. Additionally, integrating the model into the AWJ equipment control software could help determine the optimal combination of process parameters.  
The authors of the current study acknowledge some limitations of the current study. Firstly, the number of experiments used to train the model was quite low compared with regular datasets used with ANNs. The collection of data was constrained by time and material limitations. Nevertheless, other studies [49; 52; 53; 54] have successfully trained neural networks using a limited sample. The focus of this study was more data-centric and was aimed at obtaining high-quality data and the correct data using DoE. Also, a vanilla neural network was used instead of a custom-built architecture. As this was an exploratory study, the authors considered it appropriate to test a basic model and adapt it in future studies depending on the results obtained herein.  
Artificial intelligence can be successfully applied to optimize AWJ machining processes. This research demonstrates the potential of AI-driven solutions to improve the quality and efficiency of CFRP machining, opening new directions for broader applications and continuous improvement in the field.  
Conceptualization, methodology, investigation, I.A.P., V.I.B., S.S. and A.I.P.; validation, I.A.P. and A.I.P.; software, I.A.P. and V.I.B.; writing-original draft, I.A.P., V.I.B. and A.I.P.; writing--review and editing, S.S., N.B. and N.P.; research funding, N.B. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the HORIZON 2020-DiCoMI Project \"Directional Composites through Manufacturing Innovation\", GA Nr. 778068.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.