On-machine detection of face milling cutter damage based on machine vision.

Aiming at the problems of uneven illumination at the edge of the surface damage image of the face milling cutter, the blade line at the damage cannot be accurately identified, and the slow recognition rate of the traditional image processing technology, an on-machine accurate measurement method for the surface damage of the face milling cutter, is proposed. The industrial camera, lens, and adjustable LED ring lighting are placed on the camera support, and they are used to collect the image of the flank face of the face milling cutter. This method first divides the tool damage area into the tool wear bright zone area and the damaged edge missing area and selects the specific tool position area for template matching. Next, the tool damage area is extracted, the arc fitting method is used to fit the missing area of the broken edge, the edge curve is reshaped, and the improved sub-pixel edge detection method is used to extract the lower boundary of the tool wear bright zone area. The cutting edge curve of the damaged area and the lower boundary edge curve of the wear area are spliced to obtain the tool damage area, and finally, the maximum damage width value of the tool flank is calculated. The on-machine detection platform is built to carry out the milling experiment of the face milling cutter. The damage value extracted by this method is compared with the damage value measured by the super depth of field microscope. The average difference in the tool damage value is within 3%. The results show that the proposed method can effectively detect tool damage under the premise of ensuring efficiency on-line and in-process and provides an effective solution for the condition monitoring of face milling cutter in actual machining. [ABSTRACT FROM AUTHOR]