Metals (Oct 2022)

Multialgorithm Fusion for Milling Tool Abrasion and Breakage Evaluation Based on Machine Vision

  • Chao Wu,
  • Yixi Hu,
  • Tao Wang,
  • Yeping Peng,
  • Shucong Qin,
  • Xianbo Luo

DOI
https://doi.org/10.3390/met12111825
Journal volume & issue
Vol. 12, no. 11
p. 1825

Abstract

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Aiming at the problem that the current tool status monitoring system cannot measure the area of the abrasion and breakage from the milling tool images at the same time, a new detection fusion method for milling tool abrasion and breakage based on machine vision is proposed. This method divides the milling tool status into abrasion and breakage. The abrasion is recognized by an adaptive region localization growing method, and the breakage is recognized by an edge fitting reconstruction method based on distance threshold. Then, the area of tool damage can be accurately measured based on the identified abrasion and breakage information. Experiments show that the proposed method could effectively detect both the tool abrasion and breakage, and provide a better monitoring effect than that of the conventional method that only considers tool abrasion status. The proposed approach was verified by the experimental results, and the accuracy of the tool damage area characteristic was over 95%.

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