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

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.

Integration of a vision system with a robotic polishing system for machining variety of workpieces

“…Powerful Graphical User Interface (GUI) environments running on PCs coupled with image processing accelerators provide the core technologies needed for building powerful user-friendly machine vision environments with reasonable cost. The machine vision system is composed of three parts: image acquisition module, image-processing module and data communication module [22,23]. Image acquisition module application CMOS industrial camera with the corresponding lens and lighting source, access to the workpiece picture information.…”

Detection for missing small sized parts based on machine vision

“…Aiming at the problems of the existing insulator missing detection model, such as large parameters and difficult detection of small targets, Xiaobo Chi et al [4] proposed an improved lightweight detection model based on YOLOv5s, and the experiments showed that the missing insulator can be detected through the modified model. A method of missing dispensing detection method based on machine vision is proposed in [5]. The image of the product to be inspected is compared with the standard template, and the quality of the product to be inspected is judged and the location of the missing glue position is identified.…”