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Machine vision-based recognition of elastic abrasive tool wear and its influence on machining performance

Author

Listed:
  • Lei Guo

    (Chang’an University
    Chang’an University)

  • Zhengcong Duan

    (Chang’an University
    Chang’an University)

  • Wanjin Guo

    (Chang’an University
    Chang’an University)

  • Kai Ding

    (Chang’an University
    Chang’an University)

  • Chul-Hee Lee

    (Inha University)

  • Felix T. S. Chan

    (Macau University of Science and Technology)

Abstract

This study presents a novel Hunter-Prey Optimization (HPO)-optimized Otsu algorithm in tool wear assessment and machining process quality control. The algorithm is explicitly tailored to address the challenges conventional image recognition methods face when identifying the unique wear patterns of elastic matrix abrasive tools. The proposed HPO-optimized Otsu algorithm was validated through machining experiments on silicon carbide workpieces, demonstrating superior performance in wear identification, image segmentation, and operational efficiency when compared to both the conventional 2-Dimensional (2D) Otsu algorithm and the Genetic Algorithm (GA)-optimized Otsu algorithm. Notably, the proposed algorithm reduced the average runtime by 36.99% and 28.39%, and decreased the mean squared error by 24.78% and 20.52%, compared to the 2D Otsu and GA-optimized Otsu algorithms, respectively. Additionally, this study investigates the influence of elastic tool wear on abrasive machining performance, offering valuable insights for assessing tool status and life expectancy, and predicting machining quality. The high level of automation, accuracy, and fast execution speed of the proposed algorithm makes it an attractive option for wear identification, with potential applications extending beyond the manufacturing industry to any sector that requires automated image analysis. Consequently, this study contributes to both the theoretical comprehension and practical application of tool wear assessment, providing significant benefits to industries striving for enhanced production efficiency and product quality.

Suggested Citation

  • Lei Guo & Zhengcong Duan & Wanjin Guo & Kai Ding & Chul-Hee Lee & Felix T. S. Chan, 2024. "Machine vision-based recognition of elastic abrasive tool wear and its influence on machining performance," Journal of Intelligent Manufacturing, Springer, vol. 35(8), pages 4201-4216, December.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-023-02256-4
    DOI: 10.1007/s10845-023-02256-4
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    References listed on IDEAS

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    1. Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
    2. Tongwha Kim & Kamran Behdinan, 2023. "Advances in machine learning and deep learning applications towards wafer map defect recognition and classification: a review," Journal of Intelligent Manufacturing, Springer, vol. 34(8), pages 3215-3247, December.
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