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Partitioned abrasive belt condition monitoring based on a unified coefficient and image processing

Author

Listed:
  • Xiaokang Huang

    (Shanghai Jiao Tong University)

  • Xukai Ren

    (Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation)

  • Huanwei Yu

    (Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation)

  • Xiyong Du

    (Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation)

  • Xianfeng Chen

    (Shaoxing Key Laboratory of Special Equipment Intelligent Testing and Evaluation)

  • Ze Chai

    (Shanghai Jiao Tong University)

  • Xiaoqi Chen

    (Shanghai Jiao Tong University
    South China University of Technology, Guangzhou International Campus)

Abstract

Abrasive belt condition (BC) monitoring is significant for achieving profile finishing precision and quality in grinding of difficult-to-machine materials like Inconel 718. While indirect signal-based BC monitoring methods are ineffective when varying grinding parameters, existing image-based direct monitoring methods currently suffer from a lack of: (i) a unified and quantitative definition of the belt condition; (ii) in situ tool-surface image capture and relevant feature extraction; and (iii) continuous monitoring of the entire belt conditions. This paper proposes a partitioned BC monitoring method that is adaptable to ever-changing grinding conditions. Based on the belt surface analysis, a unified BC coefficient is quantitatively defined by using two critical BC-dependent features, the average area and number of worn flats of abrasive grains per unit area. The belt surface image is in-situ captured from moving belts and is preprocessed to eliminate image defects in a unified form, then the entire belt is partitioned, and finally the image features are extracted by Gabor filter and K-means clustering. The proposed robust method which has a maximum relative repeatability error of 9.33%, and less computation was validated by the experimental results. This study provides an adaptable and efficient way for continuously monitoring the conditions of the entire belt and the grinding area.

Suggested Citation

  • Xiaokang Huang & Xukai Ren & Huanwei Yu & Xiyong Du & Xianfeng Chen & Ze Chai & Xiaoqi Chen, 2024. "Partitioned abrasive belt condition monitoring based on a unified coefficient and image processing," Journal of Intelligent Manufacturing, Springer, vol. 35(2), pages 905-923, February.
    • Handle: RePEc:spr:joinma:v:35:y:2024:i:2:d:10.1007_s10845-023-02083-7
    DOI: 10.1007/s10845-023-02083-7
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    References listed on IDEAS

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    1. Yan Shen & Feng Yang & Mohamed Salahuddin Habibullah & Jhinaoui Ahmed & Ankit Kumar Das & Yu Zhou & Choon Lim Ho, 2021. "Predicting tool wear size across multi-cutting conditions using advanced machine learning techniques," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1753-1766, August.
    2. Hasan Tercan & Tobias Meisen, 2022. "Machine learning and deep learning based predictive quality in manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(7), pages 1879-1905, October.
    3. Siti Nurfadilah Binti Jaini & Deug-Woo Lee & Seung-Jun Lee & Mi-Ru Kim & Gil-Ho Son, 2021. "Indirect tool monitoring in drilling based on gap sensor signal and multilayer perceptron feed forward neural network," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1605-1619, August.
    4. Yuqing Zhou & Bintao Sun & Weifang Sun & Zhi Lei, 2022. "Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 247-258, January.
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