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Comparative analysis of different machine vision algorithms for tool wear measurement during machining

Author

Listed:
  • Mayur A. Makhesana

    (Nirma University)

  • Prashant J. Bagga

    (Nirma University)

  • Kaushik M. Patel

    (Nirma University)

  • Haresh D. Patel

    (Nirma University)

  • Aditya Balu

    (Iowa State University)

  • Navneet Khanna

    (Advanced Manufacturing Laboratory, Institute of Infrastructure Technology, Research and Management)

Abstract

Automatic tool condition monitoring becomes crucial in metal cutting because tool wear impacts the final product’s quality. The optical microscope approach for assessing tool wear is offline, time-consuming, and subject to measurement error by humans. To accomplish this, the machine must be stopped, and the tool must be removed, which causes downtime. As a result, numerous research attempts have been made to develop robust systems for direct tool wear measurement during machining. Therefore, the proposed work focused on developing a direct tool condition monitoring system using machine vision to calculate tool wear parameters, specifically flank wear. The cutting tool insert images are collected using a machine vision setup equipped with an industrial camera, bi-telecentric lens, and a proper illumination system during the machining of AISI 4140 steel. The comparative analysis of image processing algorithms for tool wear measurement is proposed under the selected machining environment. The wear boundary is extracted using digital image processing tools such as image enhancement, image segmentation, image morphology operation, and edge detection. The wear amount on the tool insert is extracted and recorded using the Hough line transformation function and pixel scanning. The comparison of results revealed the measurement accuracy and repeatability of the proposed image processing algorithm with a maximum of 6.25% and minimum of 1.10% error compared to manual measurement. Hence, the proposed approach eliminates manual measurements and improves the machining productivity.

Suggested Citation

  • Mayur A. Makhesana & Prashant J. Bagga & Kaushik M. Patel & Haresh D. Patel & Aditya Balu & Navneet Khanna, 2025. "Comparative analysis of different machine vision algorithms for tool wear measurement during machining," Journal of Intelligent Manufacturing, Springer, vol. 36(7), pages 4567-4591, October.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:7:d:10.1007_s10845-024-02467-3
    DOI: 10.1007/s10845-024-02467-3
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    References listed on IDEAS

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    1. Lucas Costa Brito & Márcio Bacci Silva & Marcus Antonio Viana Duarte, 2021. "Identification of cutting tool wear condition in turning using self-organizing map trained with imbalanced data," Journal of Intelligent Manufacturing, Springer, vol. 32(1), pages 127-140, January.
    2. Vagnorius, Zydrunas & Rausand, Marvin & Sørby, Knut, 2010. "Determining optimal replacement time for metal cutting tools," European Journal of Operational Research, Elsevier, vol. 206(2), pages 407-416, October.
    3. Rui Liu, 2023. "An edge-based algorithm for tool wear monitoring in repetitive milling processes," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2333-2343, June.
    4. Xianli Liu & Bowen Zhang & Xuebing Li & Shaoyang Liu & Caixu Yue & Steven Y. Liang, 2023. "An approach for tool wear prediction using customized DenseNet and GRU integrated model based on multi-sensor feature fusion," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 885-902, February.
    5. Paras Jain & Vipin Tyagi, 2016. "A survey of edge-preserving image denoising methods," Information Systems Frontiers, Springer, vol. 18(1), pages 159-170, February.
    Full references (including those not matched with items on IDEAS)

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