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Volume 42 Issue 2
May  2022
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Article Navigation >  Diamond & Abrasives Engineering  > 2022  >  42(2): 186-192
HU Weidong, WANG Zhankui, Dong Yanhui, ZHANG Zhao, ZHU Yongwei. Surface morphology characterization of fixed abrasive lapping pad based on deep learning[J]. Diamond & Abrasives Engineering, 2022, 42(2): 186-192. doi: 10.13394/j.cnki.jgszz.2021.0096
Citation: HU Weidong, WANG Zhankui, Dong Yanhui, ZHANG Zhao, ZHU Yongwei. Surface morphology characterization of fixed abrasive lapping pad based on deep learning[J]. Diamond & Abrasives Engineering, 2022, 42(2): 186-192. doi: 10.13394/j.cnki.jgszz.2021.0096
shu

Surface morphology characterization of fixed abrasive lapping pad based on deep learning

doi: 10.13394/j.cnki.jgszz.2021.0096
  • 1.

    Mechanical and Electrical College, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

  • 2.

    Mechanical and Electrical College, Henan Institute of Science and Technology, Xinxiang 453003, Henan, China

More Information
  • Received Date: 2021-10-24
  • Accepted Date: 2021-11-24
  • Rev Recd Date: 2021-11-20
    Abstract
  • The surface morphology of fixed abrasive (FA) lapping pad is closely related to its processing performance. In order to understand the surface morphology of the FA lapping pad better, particularly diamonds, pores, and pits resulting from diamond falling off, a deep learning-based method for characterizing its surface morphology was proposed. First, the Leica DVM6 digital microscope and its supporting software were adopted to obtain the surface images of the FA lapping pad; then python3+OpenCV were chosen to preprocess the images, and the labeling software Labelme was used to label the images for subsequent training and testing data set; finally, the Mask R-CNN model was built using the deep learning framework Tensorflow. The results show that the Mask R-CNN model can effectively segment and recognize multiple targets in the surface image of a single fixed abrasive pad, and the average accuracy of the main evaluation indicators reaches 78.9%, reaching the mainstream level of image recognition.

     

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