Research on tool tip wear detection and life prediction based on an improved L1PS model

Tool wear is a critical factor that directly impacts product performance, making accurate and timely detection essential for ensuring machining quality. In particular, under conditions of shallow cutting depth, tool tip wear significantly exceeds edge wear, yet the detection of tool tip wear has received little attention. Therefore, this paper proposes an image segmentation algorithm for detecting milling cutter tip wear, enabling precise measurement of tool tip wear. Initially, Valley-emphasis method is employed for initial segmentation of ground images to detect and segment the bottom edges. Subsequently, the detected edges serve as masks for parallel computation, achieving precise edge segmentation. Finally, the XOR result of the finely segmented edges and the mask is used to determine the wear region. Compared to existing detection algorithms, this method enhances edge detection accuracy without increasing detection time. The maximum error compared to manual measurement is within 0.007 mm, with a minimum accuracy rate of 97.92%. Additionally, the algorithm’s runtime has been reduced to 15.53 s, a decrease of approximately 94.68%. These results substantiate the efficacy of the proposed approach.