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Korea Robotics Society (한국로봇학회)
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A Study of Tool Wear Measurement Using Image Processing

이미지 프로세싱을 활용한 공구의 마모 측정법 연구

  • Sumin Kim (Changwon National University Advanced Defense Engineering) ;
  • Minsu Jung (Changwon National University Smart Manufacturing division) ;
  • Jong-kyu Park (Advanced Defense Engineering, Changwon National University)
  • Received : 2023.10.27
  • Accepted : 2024.01.03
  • Published : 2024.02.29
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Abstract

Tool wear is considered an important issue in manufacturing and engineering, as worn tools can negatively impact productivity and product quality. Given that the wear status of tools plays a decisive role in the production process, measuring tool wear is a key task. Consequently, there is significant attention in manufacturing fields on the precise measurement of tool wear. Current domestic methods for measuring wear are limited in terms of speed and efficiency, with traditional methods being time-consuming and reliant on subjective evaluation. To address these issues, we developed a measurement module implementing the DeepContour algorithm, which uses image processing technology for rapid measurement and evaluation of tool wear. This algorithm accurately extracts the tool's outline, assesses its condition, determines the degree of wear, and proves more efficient than existing, subjective, and time-consuming methods. The main objective of this paper is to design and apply in practice an algorithm and measurement module that can measure and evaluate tool wear using image processing technology. It focuses on determining the degree of wear by extracting the tool's outline, assessing its condition, and presenting the measured value to the operator.

Keywords

Acknowledgement

This deliverable is the result of research conducted under the 2022 Industry-Academia Platform Cooperative Technology Development Project (S3307399) supported by the Ministry of SMEs and Startups This research was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) (2021RIS-003)

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