Lathe Tool Wear Monitoring Method Based on Machine Vision

Abstract

A monitoring system of lathe tool wear is proposed based on machine vision in this paper. When the workpiece is processed and the tool wear images are obtained, the proposed method can calculate the tool wear value. After the image is preprocessed with noise reduction and enhancement, the GrabCut improved algorithm is used to segment the tool wear image. Aiming at the problem of the traditional Canny algorithm, the Canny edge detection operator with adaptive double thresholds is used to detect the edge of the tool wear area. Finally, the upper and lower boundaries of the tool wear area are detected by using the Hough transform method, and the wear value of the tool flank is calculated. The accuracy of the detection method is verified by experimental measurement of the surface roughness of the workpiece after machining.