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Volume 45 Issue 3
Jun.  2025
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Article Navigation >  Diamond & Abrasives Engineering  > 2025  >  45(3): 416-426
SUO Wenlong, LIN Yanfen, FANG Congfu. Surface morphology segmentation and evaluation of diamond lapping pad based on improved Mask R-CNN[J]. Diamond & Abrasives Engineering, 2025, 45(3): 416-426. doi: 10.13394/j.cnki.jgszz.2024.0080
Citation: SUO Wenlong, LIN Yanfen, FANG Congfu. Surface morphology segmentation and evaluation of diamond lapping pad based on improved Mask R-CNN[J]. Diamond & Abrasives Engineering, 2025, 45(3): 416-426. doi: 10.13394/j.cnki.jgszz.2024.0080
shu

Surface morphology segmentation and evaluation of diamond lapping pad based on improved Mask R-CNN

doi: 10.13394/j.cnki.jgszz.2024.0080
  • 1.

    College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, Fujian , China

  • 2.

    School of Data and Computer Science, Xiamen Institute of Technology, Xiamen 361021, Fujian , China

More Information
  • Received Date: 2024-05-05
  • Accepted Date: 2024-07-31
  • Rev Recd Date: 2024-07-24
    • Available Online: 2024-07-31
    Abstract
  •   Objectives  The surface morphology of diamond lapping pads has a direct impact on the lapping quality of hard and brittle materials such as sapphire and silicon carbide. Detecting and controlling the surface morphology of diamond lapping pads is a crucial step in improving the lapping quality. Collecting surface images of diamond lapping pads, which contain numerous tiny abrasive particles, pores, and complex background textures, makes it challenging to conduct quantitative detection of their surface morphology. An improved Mask R-CNN model and three parameter evaluation indicators, namely the target number recognition accuracy, target segmentation area accuracy, and target position error, are utilized to explore the segmentation performance and effect of the proposed model.   Methods  Based on the Mask R-CNN model, the dilated convolution method is adopted to improve the feature extraction network in the model's backbone. The ResNet50, which serves as the feature extraction network in the backbone, is divided into five stages: Input stem and stages 1 to 4. The network structures of the Input stem, stages 1, 2, and 4 are kept unchanged. Dilated convolution is introduced in stage 3, and each residual block in stage 3 is improved into a residual block using dilated convolution to expand the receptive field, enhance the model's ability to extract deep semantic features of abrasive particles and pore targets of smaller scales on the surface of the lapping pad, and improve segmentation performance for abrasive grain and pore targets of different scales. The loss function and mean average precision (mAP) of Mask R-CNN are used to comprehensively reflect the performance of the model. For evaluation of the segmentation effect, three parameters, namely target number recognition accuracy, target segmentation area accuracy, and target position error, are proposed. These are mainly calculated based on the number, area, and center of abrasive particles and pores, and evaluating diamond abrasive particles and pores separately to assess the overall surface morphology of the lapping pad.  Results  Through training and verification of the improved Mask R-CNN model, results show that this method can realize the recognition and segmentation of diamond abrasive particles and pores in the surface images of the lapping pad, achieving an mAP of 78.2%. By comparing the surface images of the lapping pad with the model segmentation images, there is no significant difference between the number of diamond abrasive particles and pores obtained by the improved Mask R-CNN model and the actual numbers, indicating that this method effectively recognizes and segments diamond abrasive particles and pores. Comparing the model's segmentation results with manually annotated results and calculating the three evaluation indicators, the recognition accuracies for the number of diamond abrasive particles and pores are 82.1% and 93.4%, respectively. This is due to the complex background of the lapping pad's surface images and unclear contrast between abrasive particles, pores, and binders, which can cause some missed or false detections when using this method. For successfully identified diamond abrasive grain and pore targets, the segmentation area accuracies are 89.9% and 95.3%, respectively, indicating small differences between segmented abrasive areas and actual areas, with a high degree of agreement, and good classification and segmentation performance by the model. By comparing the contours of diamond abrasive particles and pores obtained by model segmentation with the actual contours, the position errors are 3.80% and 2.80%, respectively, indicating small differences between the segmented and actual contours, and demonstrating good segmentation accuracy.   Conclusions  The dilated convolution method can effectively expand the receptive field and improve the ability to extract deep semantic features of targets at different scales. Therefore, based on the comparison between the segmentation images of the improved Mask R-CNN model and manually annotated images and the evaluation of the three indicators, the improved Mask R-CNN model demonstrates good segmentation performance for diamond abrasive particles and pores of different scales on the lapping pad surface, proving the effectiveness of the segmentation method.

     

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