## 4 Discussion  
The integration of AE signals into adaptive control systems for CNC wood milling represents a significant advancement in machining technology. This study aimed to investigate the applicability of AE signals for real-time monitoring and adaptive control in CNC wood milling processes. The findings provide valuable insights into the potential of AE-based monitoring systems to enhance machining efficiency, tool life, and product quality. AE signals are generated by the rapid release of energy from localized sources within a material, such as micro-fractures or dislocations. In the context of CNC wood milling, these signals can provide real-time information about the machining process, including tool condition, material behavior, and cutting dynamics. This study demonstrated that AE signals are sensitive to variations in cutting parameters, such as feed rate and spindle speed, and can effectively reflect changes in the machining environment. The experimental setup, utilizing a five-axis CNC machining center and dual-channel AE sensors, allowed for the collection of high-frequency AE data during side milling of medium-density fiberboard (MDF). The analysis of AE signal characteristics, including root mean square (RMS) values and signal envelope (MAX_sig), revealed distinct patterns corresponding to different cutting conditions. These findings align with previous research [33; 38; 42; 43] indicating the potential of AE signals to monitor machining processes in real-time. The complexity and non-stationary nature of AE signals necessitate advanced signal processing techniques for effective analysis. In this study, Fast Fourier Transform (FFT) was applied to transform AE signals from the time domain to the frequency domain. This approach facilitated the identification of dominant frequencies and allowed for the assessment of tool engagement and cutting efficiency. The results indicated that variations in cutting conditions, such as changes in spindle speed and feed rate, significantly influenced the frequency characteristics of AE signals. For instance, at higher spindle speeds and feed rates, the AE signals exhibited increased amplitudes and distinct frequency peaks, suggesting enhanced tool engagement and material removal rates. Conversely, at lower cutting parameters, the AE signals displayed reduced amplitudes and broader frequency distributions, indicating decreased cutting efficiency. These observations are consistent with findings from other studies that have utilized AE signals to monitor cutting forces and tool wear in machining processes [48; 49; 50; 51; 52; 53]. The ability of AE signals to capture dynamic changes in the machining environment underscores their potential as a reliable input for adaptive control systems. The present study demonstrates that AE signals (specifically RMS) capture meaningful differences in CNC wood-milling regimes and therefore hold promise for adaptive process control. The statistical analysis revealed a highly significant overall effect of cutting conditions on AE signal magnitude, confirming that spindle speed and feed rate produce a distinct AE response. The non-significant comparison occurred between the cutting conditions using the same feed rate, indicating that their AE responses were similar. In practice, this means that an adaptive controller for CNC wood milling using RMS thresholds can reliably detect transitions across different spindle speeds but may require additional features or multivariate criteria to distinguish conditions, especially at lower feed rates. Increasing the feed rate from 6 m\(\cdot\)min\({}^{-1}\) to 12 m\(\cdot\)min\({}^{-1}\) resulted in a roughly 3.1 dB rise in the RMS value (from approximately 65.5 dB to 68.6 dB in the original data) and a corresponding increase in envelope (MAX_sig) amplitude. This increase suggests that doubling the feed rate increases the instantaneous chip load per tooth, producing more energetic fiber-fracture events and therefore higher AE energy per revolution.