## 1 Introduction
Acoustic emission (AE) encompasses phenomena in which a sudden release of energy at specific sites within a material produces brief elastic waves which can themselves trigger further transient waves [39]. The ability of AE to detect and characterize dynamic events occurring during machining makes it an ideal signal for adaptive control in CNC wood machining process. This makes AE particularly valuable for detecting early signs of tool wear or changes in cutting conditions that would otherwise go unnoticed by other monitoring techniques. Recorded AE signals are characterized by four basic parameters--average frequency, peak amplitude, rise time, and ring-down count--which in turn depend on the sensor's frequency response and damping, the propagation medium, the structure's frequency response, amplifier gain, voltage threshold, and overall sensor sensitivity [40]. Furthermore, both how and where AE sensors are installed critically influence the effectiveness of the monitoring technique [41]. Several studies have demonstrated the effectiveness of AE in CNC wood machining, particularly for monitoring tool condition and optimizing cutting parameters. [PERSON] et al. [33] evaluated and quantified the surface quality and cutting energy con sumption in radiata pine while varying the slope of the grain. They also quantified the relationship between surface roughness and acoustic emission under different cutting conditions. [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) developed an intelligent framework for airborne dust monitoring using AE sensors during the circular sawing process of Douglas-fir wood. [PERSON] et al. [PERSON] et al. (2018) reviewed developments in the field of online monitoring of saw surface quality, sawing vibration, and saw blade conditions, as well as their application in automation control technology using AE monitoring. [PERSON] et al. [PERSON] et al. (2018) introduced a sensor-fusion strategy that combines airborne sound, cutting force, power consumption, and AE data during the milling of various wood-based materials, aiming to precisely predict workpiece properties such as wood density for strength grading and surface roughness to detect tool wear or suboptimal machining conditions. On the contrary, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) investigated the potential usefulness of vibro-acoustic signals for the indirect assessment of the geometrical and technological indicators of tool wear during laminated chipboard milling. They reported that the AE signal, measured by a contact sensor attached to the workpiece, was found to be completely useless as a source of information about any aspect of the tool wear problem. Furthermore, [PERSON] et al. [PERSON] et al. (2018) evaluated vibro-acoustic signals for tool wear monitoring using a multiple regression technique in compreg milling. They found that AE signals were wholly inadequate for sensing tool wear. Despite its potential, the use of AE in adaptive control systems for CNC wood milling presents several challenges. One of the primary challenges lies in the interpretation of AE signals, which can be influenced by a wide range of factors, including the type of material being machined, cutting tool geometry, machining parameters, and even environmental factors such as temperature and vibration. As a result, extracting meaningful information from AE signals requires sophisticated signal processing techniques, such as wavelet analysis, Fourier transforms, and machine learning algorithms. These techniques are essential for identifying patterns and distinguishing between different sources of acoustic emissions, enabling accurate real-time predictions of machining events. Another challenge is the integration of AE-based signals with existing CNC control systems. While AE sensors provide valuable data, they must be seamlessly integrated into adaptive control algorithms to influence the machine's decision-making process.