## 5 Conclusions  
The aim of the present study was to assess the feasibility of utilizing acoustic emission (AE) signals as the primary feedback mechanism for adaptive control in the CNC milling of medium-density fiberboard (MDF). Four distinct cutting scenarios were investigated, combining two spindle speeds (10,000 rpm and 20,000 rpm) with two feed rates (6 m\(\cdot\)min\({}^{-1}\) and 12 m\(\cdot\)min\({}^{-1}\)). Dual-sensor AE data were acquired during each milling pass, and time-domain root-mean-square (RMS) values as well as envelope maxima (MAX_sig) were computed. To eliminate the confounding effect of differing numbers of spindle revolutions per cut, AE metrics were normalized on a per-revolution basis. Comprehensive statistical analyses were employed to discern the influence of cutting parameters on AE characteristics.  
This study establishes that acoustic emission signals, particularly normalized time-domain RMS and envelope metrics, serve as sensitive and reliable indicators of feed-related cutting forces in MDF milling, while remaining effectively invariant to moderate spindle speed changes. These characteristics render AE monitoring well suited for integration into adaptive control loops aimed at optimizing feed, extending tool life, and ensuring consistent surface quality. Therefore, AE-based monitoring systems can play a pivotal role in advancing smart manufacturing practices and achieving higher levels of automation and efficiency in the woodworking industry.  
Future research should focus on addressing the challenges associated with the variability of AE signals and the integration of AE-based monitoring systems into existing CNCinfrastructure. Developing standardized protocols for AE sensor calibration and signal processing can enhance the reliability and consistency of AE-based monitoring systems. Additionally, exploring the fusion of AE signals with other sensor data, such as vibration, temperature, and cutting force, can provide a more comprehensive understanding of the machining process. Machine learning algorithms and artificial intelligence techniques can be employed to analyze multi-sensor data and develop predictive models for adaptive control in CNC wood milling.  
Conceptualization, M.D. and P.K.; methodology, M.D. and F.V.; software, F.V.; validation, J.S.; formal analysis, P.K.; investigation, M.D. and F.V.; resources, P.K. and F.V.; data curation, F.V.; writing--original draft preparation, M.D. and J.S.; writing--review and editing, P.K. and F.V.; visualization, J.S.; supervision, F.V.; project administration, P.K.; funding acquisition, P.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Slovak Research and Development Agency, Grant Number APVV-20-0403, for the FMA analysis of potential signals suitable for the adaptive control of nesting strategies for milling wood-based agglomerates.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author, [PERSON], upon reasonable request.  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.