Efficient Cutting Tool Wear Segmentation Based on Segment Anything Model

Tool wear conditions impact the surface quality of the workpiece Tool wear is an inevitable phenomenon in the actual machining and its final geometric precision. In this research, we process. It leads to alterations in the cutting zone's process propose an efficient tool wear segmentation approach based on variables like the forces and temperatures exerted on both the tool Segment Anything Model, which integrates U-Net as an automated and workpiece. These conditions not only influence the rate of prompt generator to streamline the processes of tool wear tool wear but also affect the surface quality and geometric precision detection. Our evaluation covered three Point-of-Interest generation of the workpiece [1]. Therefore, tool wear is one of the methods and further investigated the effects of variations in key determinants of both tool costs and the quality of the finished training dataset sizes and U-Net training intensities on resultant workpiece, emphasizing the necessity for monitoring during the wear segmentation outcomes. The results consistently highlight machining process to ensure optimal outcomes [1, 2].