## 1 Introduction
Proper selection and adjustment of these angles, along with controlling chip thickness, optimize cutting conditions and improve productivity in milling. Tool wear is an inevitable phenomenon affecting the efficiency, surface finish quality, and dimensional accuracy of machined parts. During milling, both the cutting tool and workpiece experience intense forces and frictions, resulting in various wear types. Optimizing cutting parameters like cutting speed (\(v_{c}\) [m/min]), feed rate (\(f_{z}\) [mm/rev]), and depth of cut (\(a_{p}\) [mm]) can minimize the forces and heat generated during the process. Understanding common wear types is vital for optimizing machining processes and selecting appropriate materials and cutting parameters. The wear of cutting tools is a problem that can directly impact product quality, process efficiency, and, consequently, cost. Tool wear is an unavoidable phenomenon that primarily occurs due to friction forces, high temperatures, and adverse working conditions, eventually leading to a loss of precision and tool failure. This is why the prevention and management of tool wear in milling is a critical factor to consider in ensuring the continuity and quality of production. The study of cutting tool wear, particularly in milling machines, has been a constant point of interest in the manufacturing industry and has advanced significantly over time, encompassing everything from traditional monitoring and analysis methods to the application of cutting-edge AI techniques. Each stage has contributed to improving the accuracy and effectiveness of wear control. Initially, cutting tool wear was assessed using rather rudimentary methods, such as visual inspection and direct measurement of deterioration on inserts using microscopes and high-resolution cameras [6]. While these techniques were useful for identifying evident damage to the tools, they had significant limitations. First, the process was labor-intensive,as it required the removal of the tools from the machine for detailed inspection, which disrupted production and increased downtime, thus reducing overall process efficiency. Additionally, the subjectivity tied to visual inspection could lead to inconsistent results, depending on the experience and judgment of the operator. In short, the need for constant monitoring and the lack of predictive capability limited their effectiveness in large-scale production scenarios. However, despite these limitations, these methods were fundamental in the initial phase of wear study, providing a foundation for the development of more advanced techniques that emerged over time. As technology advanced and the need for improved production efficiency grew, more precise and less intrusive methods for wear evaluation were introduced. Among these, cutting force measurement became a popular technique, as it allowed for inference of tool wear conditions by monitoring the stresses experienced during the milling process. Similarly, vibration detection offered a way to identify anomalies in tool behavior that could indicate excessive wear or impending failure [6]. Monitoring the temperature in the cutting zone was also adopted as an indirect indicator of wear, given that an increase in temperature is usually associated with higher friction and, consequently, greater deterioration of cutting tools [7]. With the advent and improvement of advanced sensors and data acquisition systems, tool wear control became more precise and automated. Force, acoustic, thermal, and vibration sensors were integrated into milling machines to capture real-time data on operational conditions and the state of the tools [8]. These control systems, working alongside signal processing techniques, enabled earlier detection of wear and a better understanding of the mechanisms that cause it without the need to stop the machine for inspections. Despite these advances, the interpretation and analysis of the data remained significant challenges due to the complexity of wear patterns and the multiple variables involved.