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A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques

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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.

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Authors and Affiliations

  1. University of León, León, Spain

    María Teresa García-Ordás, Enrique Alegre & Rocío Alaiz-Rodríguez

  2. University of Edinburgh, Edinburgh, UK

    Víctor González-Castro

Authors
  1. María Teresa García-Ordás
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  2. Enrique Alegre
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  3. Víctor González-Castro
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  4. Rocío Alaiz-Rodríguez
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Correspondence to María Teresa García-Ordás.

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García-Ordás, M.T., Alegre, E., González-Castro, V. et al. A computer vision approach to analyze and classify tool wear level in milling processes using shape descriptors and machine learning techniques. Int J Adv Manuf Technol 90, 1947–1961 (2017). https://doi.org/10.1007/s00170-016-9541-0

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  • DOI: https://doi.org/10.1007/s00170-016-9541-0

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