Research on automatic monitoring method of face milling cutter wear based on dynamic image sequence

Tool wear is an important factor affecting the quality of finished products, productivity, and the normal operation of machine tools, so tool condition monitoring (TCM) has become a research hotpot in the field of intelligent manufacturing. Compared with traditional monitoring methods, vision-based tool condition monitoring methods are more accurate and intuitive. However, the existing visual monitoring method requires manual adjustment of the tool position, and the degree of automation needs to be improved. Therefore, this paper proposes automatic face milling cutter condition monitoring method based on dynamic image sequence. We first acquire the dynamic image sequence of face milling cutter with the spindle rotating, then forward the dynamic image sequence to the image processing module to extract target area. And the images after image processing are propagated to the image selection module to obtain the image to be measured. Finally, forward the selected image to wear value measurement module to obtain the wear value. The presented automatic face milling cutter condition monitoring method is verified on a five-axis milling center. Compared with the direct measurement results of industrial digital microscope, the measurement error of the proposed method is within 4%, which is a reliable and effective online monitoring method for milling cutter wear.