## 1 Introduction  
Tool wear is the result of cutting temperature, cutting force, and mechanical friction in the milling process of Computer Numerical Control (CNC) machines [1; 2]. Tool wear reduces the workpiece quality and increases the workpiece surface roughness [3]; serious tool wear causes chatter, fracturing, and chipping, which damages both the machine tools and the workpiece, and can also lead to serious processing accidents [4]. Therefore, appropriate monitoring and classification mechanisms could help to decrease the loss caused by tool wear, and to obtain better surface quality [5]. By modifying the structure of solid materials, the machining process is the most significant industrial method for producing semi-finished and final outputs. Cutting parameters are typically utilized to eliminate chips in the substance [6]. However, even though advanced technologiesin cutting tool compositions and milling machine control technologies have resulted in extended mechanical properties, the instruments eventually wear down with time [7]. Tool wear invariably has a direct impact on the surface polish and predicted probability of the completed workpiece, resulting in failures [8]. Tool removal and unplanned shutdowns because of worn tools could even result in machine breakdown and product inconsistencies, and eventually in financial loss. As a result, it is critical to avoid unscheduled downtime throughout operation, which has a significant effect on organizational performance [9]. Tool condition monitoring methodologies have been adopted for nearly two decades to minimize this, and they have proved themselves as a basic requirement of modern machine tools [10]. Coated tools are more widely utilized in numerous machining operations than uncoated tools because they have a higher tool performance and increased processability [11]. The remaining thickness of coated tools throughout manufacturing operations might be beneficial for the technician to monitor when using worn tools [12]. To determine the depth of a coating layer, aggressive tools such as microhardness monitoring and metallography would be used, although they are costly, time-consuming, and difficult to utilize [13]. The tool wear monitoring is categorized into direct approaches and indirect approaches based on the hardness of the machining process [14]. The direct approaches based on the measurement of flank wear consists of electrical resistance, radioactivity, and vision inspection [15]. The direct approaches are not conducive to the practical applications, due to the limitation of accessing similar levels of illumination, cutting fluid, and the presence of chips, and high requirements must be reached to measure the environment [16]. Therefore, indirect approaches are effective in monitoring tool wear based on image analysis. The computer vision approach directly measures tool wear, which helps in achieving high levels of reliability and precision [17; 18]. Nonetheless, the bulk of tool condition measurement techniques reported in currently published studies have utilized complex, non-production-ready sensing technologies, including force dynamometers [19]. Due to the diversity of signals that must be acquired from the operating equipment, even with sophisticated data-gathering hardware, collecting high-quality datasets to train the machine is challenging and time-consuming [20]. Cutting tools are typically used to decrease cutting temperature and friction during machining processes to increase tool life and surface quality. Water-oil emulsions, with emulsifiers and additives to stop corrosion and bacterial development, make up the majority of cutting fluids. Poisonous compounds are present in the emulsions, machine oils, and the heavy metals that are combined with the fluids during the machining process. In order to lessen the impact on the environment, cutting fluid application must be reduced and hazardous components must be eliminated.