A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning ... View Full Text


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Article Info

DATE

2017-05

AUTHORS

María Teresa García-Ordás, Enrique Alegre, Víctor González-Castro, Rocío Alaiz-Rodríguez

ABSTRACT

In this paper, we present a new approach to categorize the wear of cutting tools used in edge profile milling processes. It is based on machine learning and computer vision techniques, specifically using B-ORCHIZ, a novel shape-based descriptor computed from the wear region image. A new Insert dataset with 212 images of tool wear has been created to evaluate our approach. It contains two subsets: one with images of the main cutting edge and the other one with the edges that converge to it (called Insert-C and Insert-I, respectively). The experiments were conducted trying to discriminate between two (low-high) and three (low-medium-high) different wear levels, and the classification stage was carried out using a support vector machine (SVM). Results show that B-ORCHIZ outperforms other shape descriptors (aZIBO and ZMEG) achieving accuracy values between 80.24 and 88.46 % in the different scenarios evaluated. Moreover, a hierarchical cluster analysis was performed, offering prototype images for wear levels, which may help researchers and technicians to understand how the wear process evolves. These results show a very promising opportunity for wear monitoring automation in edge profile milling processes. More... »

PAGES

1947-1961

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00170-016-9541-0

DOI

http://dx.doi.org/10.1007/s00170-016-9541-0

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https://app.dimensions.ai/details/publication/pub.1022934326


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