## 4 Discussion
By contrast, when the spindle speed was doubled from 10,000 rpm to 20,000 rpm at a constant feed of 6 m\(\cdot\)min\({}^{-1}\), the RMS remained essentially unchanged. Thus, across mechanistic,-fracture-dynamics, and statistical viewpoints, feed rate unequivocally governs AE signal amplitude in MDF milling, whereas spindle speed changes primarily affect cumulative energy through revolution count without significantly altering per-revolution AE intensity. In line with [PERSON] et al. [54], this suggests that for two cutting conditions with an identical feed rate, the chip thickness imparted to each cutting edge remains constant, thereby yielding equivalent material removal per tooth engagement. Because AE energy originates predominantly from brittle fiber-fracture events whose magnitude scales with chip thickness, the per-revolution energy release is invariant for a given feed rate. Although increasing spindle speed reduces chip thickness per tooth, it simultaneously increases the tooth-pass frequency so that the total number of fracture events per revolution remains unchanged. Consequently, when AE metrics are normalized on a per-revolution basis, both the RMS amplitude and envelope maxima exhibit statistically indistinguishable values for any runs conducted at the same feed rate, irrespective of spindle speed. While the current study offers valuable insights into the applicability of acoustic emission (AE) signals for adaptive control in CNC wood milling, it is important to critically assess its limitations. Recognizing these shortcomings not only contextualizes the results but also provides a foundation for refining future experimental designs and improving system integration in industrial settings. The following subsection identifies and discusses key experimental limitations across various dimensions of the study, including material and tool variability, sensor configuration, signal processing, environmental control, and the limitations inherent in generalizing the findings to industrial-scale adaptive control systems. A significant limitation of this experiment lies in the choice and uniformity of the workpiece material--commercially available MDF. While MDF offers a relatively consistent internal structure compared to natural wood, it remains a composite material with inherent density and structural variability due to fiber orientation, resin distribution, and moisture content fluctuations. The material property table (Table 1) suggests a \(\pm\)7% variability in density, which may introduce inconsistencies in AE signal behavior independent of cutting parameters. Additionally, the use of a single material type (MDF) restricts the generalizability of the results. Wood, as a biologically heterogeneous material, presents a wide range of responses in AE activity based on species, grain orientation, moisture content, and resin content. The experimental results from MDF milling may not directly extrapolate to other wood-based materials like solid wood, plywood, or oriented strand board (OSB). Consequently, the potential of AE signals for adaptive control in a broader context remains insufficiently tested. Another experimental limitation is the lack of the explicit characterization or variation of cutting tool geometries and conditions. Although it is mentioned that tool engagement influences the AE signal, the study does not systematically account for tool wear or blade integrity across repeated cuts. AE signals are known to be highly sensitive to changes in tool sharpness, edge chipping, and coating degradation. Without quantifying tool wear or employing worn versus new tools for comparative analysis, it is difficult to determine whether observed variations in AE amplitude and frequency distributions stem from machining parameters or a gradual degradation in the cutting edge. This oversight limits the interpretation of AE data as a reliable indicator of tool condition--one of the main promises of AE-based monitoring.