Combining shape and contour features to improve tool wear monitoring in
  milling processes
M. T. García-Ordás E. Alegre-Gutiérrez V. González-Castro R. Alaiz-Rodríguez
In this paper, a new system based on combinations of a shape descriptor and a
contour descriptor has been proposed for classifying inserts in milling
processes according to their wear level following a computer vision based
approach. To describe the wear region shape we have proposed a new descriptor
called ShapeFeat and its contour has been characterized using the method
BORCHIZ that, to the best of our knowledge, achieves the best performance for
tool wear monitoring following a computer vision-based approach. Results show
that the combination of BORCHIZ with ShapeFeat using a late fusion method
improves the classification performance significantly, obtaining an accuracy of
91.44% in the binary classification (i.e. the classification of the wear as
high or low) and 82.90% using three target classes (i.e. classification of the
wear as high, medium or low). These results outperform the ones obtained by
both descriptors used on their own, which achieve accuracies of 88.70 and
80.67% for two and three classes, respectively, using ShapeFeat and 87.06 and
80.24% with B-ORCHIZ. This study yielded encouraging results for the
manufacturing community in order to classify automatically the inserts in terms
of their wear for milling processes.