Research on online detection System of lathe tool wear based on Machine Vision

Abstract

Abstract Machine tools are important factor to determine the surface quality of the workpiece, and the online detection of tool wear is of great significance to the production and processing. In this paper, turning tools are taken as the research object, the tool wear evaluation index is defined, and the online detection system of lathe tool wear based on machine vision is designed. The workpiece processing, tool wear image acquisition, transmission, storage, and processing are completed in this system. Aiming at the problem of tool wear state detection, an adaptive hybrid filtering method is proposed in order to remove noise in the image acquisition process, nonlinear transformation and unsharp masking methods are used to enhance tool wear image quality. The GrabCut improved algorithm is used to segment the tool wear image. 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, which is compared with the blunt standard VB=06mm to determine whether the tool needs to be replaced. The accuracy of the detection method is verified by experimental measurement of the surface roughness of the workpiece after machining.