Tool wear monitoring using an online, automatic and low cost system based on local texture

Abstract

[EN] In this work we propose a new online, low cost and fast approach based on computer vision and machine learning to determine whether cutting tools used in edge pro le milling processes are serviceable or disposable based on their wear level. We created a new dataset of 254 images of edge pro le cutting heads which is, to the best of our knowledge, the rst publicly available dataset with enough quality for this purpose. All the inserts were segmented and their cutting edges were cropped, obtaining 577 images of cutting edges: 301 functional and 276 disposable. The proposed method is based on (1) dividing the cutting edge image in di erent regions, called Wear Patches (WP), (2) characterising each one as worn or serviceable using texture descriptors based on di erent variants of Local Binary Patterns (LBP) and (3) determine, based on the state of these WP, if the cutting edge (and, therefore, the tool) is serviceable or disposable. We proposed and assessed ve di erent patch division con gurations. The individual WP were classi ed by a Support Vector Machine (SVM) with an intersection kernel. The best patch division con guration and texture descriptor for the WP achieves an accuracy of 90.26% in the detection of the disposable cutting edges. These results show a very promising opportunity for automatic wear monitoring in edge pro le milling processes. Keywords: Tool wear, texture description