A novel algorithm for tool wear online inspection based on machine vision

  • Qiulin Hou
  • Jie SunEmail author
  • Panling Huang


To inspect the tool wear condition in the process of numerical control (NC) machining for difficult-to-cut material, an online inspection system for tool wear is developed based on machine vision. With the help of MATLAB software, a self-matching algorithm is proposed according to the characteristics of tool wear images. The corresponding user-friendly graphical user interface (GUI) of the algorithm is developed. The bottom edges are separated by the adaptive connecting domain labeling to analyze wear condition of each edge. Then, each edge is arranged regularly by the improved rotatory positioning. The cutting edge is extracted by the method of partial angle threshold to fit and calculate the bottom wear value. It is shown that the absolute values of errors on the maximum wear width are less than 0.007 mm by using the self-matching algorithm. In the case of severe wear and breakage, the absolute values of errors on the maximum wear width are less than 0.057 mm because of uneven reflected light. The system features high response speed, high inspecting accuracy, and anti-noise performance. It is proved to be able to increase the utilization of cutting tools and guarantee the quality of workpiece. This method is potential to guarantee the reliability of cutting tool in aerospace manufacturing.


Tool wear Machine vision Image processing Self-matching algorithm Online inspection 


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The authors want to extend sincere thanks to all the previous researches that contributed to this paper.

Funding information

The authors would like to thank the support of the programs AVIC Cheng Du Aircraft Industrial (Group) Co., Ltd. (no. 2014-063), Key R & D project of Shandong Province (grant no. 2017GGX30141), and the digital workshop of aeronautic large-scale complex structural (intelligent manufacturing special support by the Ministry of Industry and Information Technology in 2015) (no. 40205000150X).


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Mechanical EngineeringShandong UniversityJinanChina
  2. 2.Key Laboratory of High Efficiency and Clean Mechanical Manufacture of Ministry of EducationShandong UniversityJinanChina

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