  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 profile milling processes are serviceable or disposable based on their
wear level. We created a new dataset of 254 images of edge profile cutting
heads which is, to the best of our knowledge, the first 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 different regions, called Wear Patches (WP), (2)
characterising each one as worn or serviceable using texture descriptors based
on different 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 five different patch
division configurations. The individual WP were classified by a Support Vector
Machine (SVM) with an intersection kernel. The best patch division
configuration 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 profile milling
processes.
