## 1 Introduction  
In modern manufacturing, ensuring the reliability and precision of milling machines is vital due to their wide use in various industries [1]. Milling machines play a key role in transforming raw metal workpieces into finished products by performing complex operations [2, 3]. These operations, characterized by high speeds and heavy loads, make milling machines susceptible to unexpected faults, particularly in main components such as cutting tools, gears, and bearings. Among the various faults encountered, mechanical failure accounts for 57% of all issues, with bearing failures representing 42% of these cases. Furthermore, faults in gears and bearings can disrupt the functionality of the spindle system, leading to downtime and financial losses. For instance, cutting tool failures contribute to approximately 20% of unexpected downtime, significantly impacting operational efficiency [4]. Mechanical component failures in milling machines not only increase maintenance costs but also lead to longer downtime, injuries, fatalities, and reduced cutting speeds [5]. As such, addressing these faults promptly is essential for maintaining operational efficiency and minimizing financial impacts. This study focuses on developing an intelligent fault diagnosis technique to detect and identify mechanical component failures in milling machines, specifically targeting gears, bearings, and cutting tools.  
Over the past decade, intelligent condition monitoring techniques have transformed manufacturing by enhancing efficiency, sustainability, and profitability [6]. Condition monitoring involves the real-time assessment of machine health using sensors and artificial intelligence (AI). The literature identifies two major technologies for machining condition monitoring: direct and indirect methods. Direct methods analyze variations in tool geometry, such as flank wear and surface quality, using techniques like machine vision [7]. Despite their accuracy, direct methods are less favored due to their long down-time, susceptibility to cutting fluids, limited access to machine parts, and dependence on lighting conditions. In contrast, indirect methods utilize sensors to capture physical signals, such as vibrations [8] and acoustic emissions (AEs) [9], which are then processed using advanced signal processing and AI algorithms. AE-based monitoring has several advantages over conventional techniques, as it operates at higher frequencies, reducing interference from machine vibrations and environmental noise [10]. The frequency range of the AE signal is above that of vibrations from machine tools and any noise interference from the surroundings of the milling machine.  
The shear stress in the material, which results from the interaction between the tool and the workpiece during machining, generates acoustic emissions. These emissions can characterize changes in material removal, chip formation, and tool wear generation. Furthermore, AE signal analysis allows for the monitoring of critical rotating machinery within the milling machine due to its non-directional nature. AE sensors are easy to install and are less impacted by tool geometry and cutting circumstances. Compared to other direct and indirect non-destructive testing techniques, AE is an appealing option for industrial applications because of these special qualities. Therefore, this study uses AE-based non-destructive testing to monitor essential components, such as bearings, gears, and cutting tools, within milling machines.