## 1 Introduction
* The orifice and focusing tube are subject to wear during abrasive waterjet machining [24; 27]. The orifice is susceptible to sudden breakage, cracking, or tearing. * A water pressure drop refers to a reduction in the force exerted by the water stream used in the AWJ process. Such a decrease can appear due to various factors, including leaks in the system, blockages in the water supply lines, or malfunctions in the pump mechanisms [26]. These malfunctions can result in CM delamination, reduced accuracy, and poor surface quality. Delamination leads to part rejection and increased expenses related to material wastage [5; 9; 24; 27]. To avoid these issues, a real-time monitoring system may be a solution [24; 27]. Acoustic Emission (AE) testing is a non-destructive evaluation (NDE) technique (EN1330-9) used to monitor and analyze the high-frequency stress waves emitted by materials under stress [28; 29]. These stress waves, or acoustic emissions, occur when a material undergoes deformation, crack formation, or other structural changes. AE testing is highly sensitive and can detect the early signs of material fatigue, fractures, and other anomalies that may not be visible on the surface [30]. AE testing is widely used across various industries, including aerospace, civil engineering, manufacturing, and power generation, due to its ability to provide real-time monitoring and early warnings of potential failures [27; 30; 31]. AE has proven to be successful in monitoring the AWJC process [27; 28; 29; 30]. This testing method detects and analyzes high-frequency (20 kHz-2 MHz) and low-amplitude energy waves generated during AWJC [28]. Fine changes in the AWJ process are observed in AE signal characteristics. The AE signal characteristics are also correlated with the AWJ process parameters. The _AERMS_ and _peak-to-peak_ values (_maximal amplitudes_) are dependent on the _feed rate_, particularly in the case of cutting AISI 309 material [30]. [PERSON] et al. investigated the correlation between the AWJ cutting parameters and the AE signal and observed a correlation between the feed rate, _AERMS amplitude_, and surface quality [31]. [PERSON] et al. were able to detect cutting head clogging with abrasive particles by monitoring the _root mean square_ (_RMS_) of the AE signal [24]. [PERSON] et al. developed a monitoring system based on _AERMS_ analysis to detect cutting head wear. They obtained clear patterns in the AE signal and concluded that more research is needed to develop an algorithm that automatically determines the upper and lower limits of _AERMS_ values [27]. [PERSON] et al. proposed and demonstrated a theory stating that the energy of the AWJ correlates and is proportional to the AE signal energy. They concluded the study by emphasizing the necessity of the mathematical modeling of AE [28]. The mathematical modeling of the AWJ process is complex due to the numerous parameters and variables involved. These models aim to predict various aspects of the AWJ process, such as material removal rate, kerf geometry, surface roughness, or jet penetration depth [32; 33; 34]. Empirical models, derived from experimental data and statistical analysis, are valuable tools for predicting the performance of the AWJ process. These models have demonstrated a good prediction accuracy, often with errors under 10%. However, a common limitation is their reliance on a relatively small number of variables [13; 33]. Existing analytical models are based on fundamental principles of fluid mechanics, solid mechanics, and abrasive particle kinetics. These analytical models of AWJC also incorporate empirical correlations and experimental data to enhance their accuracy and applicability [32]. Artificial Neural Networks (ANNs) are employed to model the AWJ process, with predicted errors typically under 15% [35], demonstrating effectiveness [36]. The integration of AWJC process monitoring into the framework of digitalization and Industry 4.0/5.0 can significantly enhance operational efficiency [37], quality control, and predictive maintenance [38; 39].