Attention Mechanism based CNC milling cutter wear detection using machine vision

With the development of modern manufacturing industry, the requirements for machining accuracy of CNC machine tools are becoming increasingly high. Traditional wear detection technology is gradually unable to meet the requirements of detection accuracy and speed, so there is an urgent need for intelligent and efficient detection methods. A CNC milling tool wear detection system is developed to address the problem of tool wear during the CNC milling process. A scheme is designed for the image acquisition system of the side and bottom edges of the milling cutter based on machine vision. An attention mechanism-based method is proposed for milling cutter wear detection. An experimental platform and completed experiments were established on image registration and fusion, tool edge extraction, and tool wear classification. The experimental results show that: The method can classify the tool wear status, extract the side edge surface contour, and improve the accuracy and efficiency of digital milling.