Machine Vision Based Study on State Recognition of Milling Cutter

Abstract

As a major cutting tool in machining, the wear state of a milling cutter has a directly impact on machining quality and production efficiency. In order to detect the wear conditions of milling cutter, an inspection platform based on machine vision principle is designed. Hardware arrangement of the platform is introduced and the related equipment is analysed and selected accordingly. The milling cutter clamping device used in the platform is also designed. The collection of the measured milling cutter end image is realized successfully. The image processing methods of worn cutter are studied in detail. Adaptive greyscale adjustment and median filtering are used to decrease the noise in the image. Automatic threshold segmentation and edge extraction based on Sobel operator are also designed to achieve image pre-processing. A series of image processing experiments are carried out. The feature extraction algorithm for worn milling cutter end image is presented. Through comparative experiment, the presented machine vision measurement method is efficient.