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Efficient-Unet: Intelligent identification of abrasive grain on the entire surface of monolayer brazing wheel based on encoder–decoder network

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Abstract

Measuring and extracting abrasive grains on the entire surface of monolayer brazing grinding wheels to analyze the distribution of abrasive grains are of great significance to grinding research and grinding wheel manufacture. It is not easy to carry out the work with traditional methods. In this paper, a linear CCD is used to acquire the entire grinding wheel surface image, and an improved encoder–decoder network based on Efficientnet, ASPP, and Skip Connections is designed to promote the accuracy and speed of abrasive grains prediction. Based on dataset creation, transfer learning, and hyper-parameter testing, 91.21% abrasive grain semantic segmentation accuracy was finally obtained. If we focus on whether the abrasive grains are recognized without considering semantic errors, an accuracy rate of 99.6% is obtained. The method can provide basic data for grinding mechanism research and abrasive tool manufacturing.

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Funding

The authors received support from the Natural Science Foundation of Fujian, China (Grant No. 2021J01855, 2021J05168, 2022H0020); doctoral research fund of Jimei University (Grant No. ZQ2021055); and Jimei University cultivate program of National Nature Science Foundation of China (Grant No. ZP2022013).

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Authors and Affiliations

  1. College of Marine Equipment and Mechanical Engineering, Jimei University, Xiamen, 361021, China

    Junying Chen, Boxuan Wang, Yiming Lin, Xiuyu Chen & Qingshan Jiang

  2. Institute of Manufacturing Engineering, Huaqiao University, Xiamen, 361021, China

    Changcai Cui

Authors
  1. Junying Chen
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  2. Boxuan Wang
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  3. Yiming Lin
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  4. Xiuyu Chen
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  5. Qingshan Jiang
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  6. Changcai Cui
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Contributions

Junying Chen: general scheme, writing the original draft, resources, and funding acquisition. Boxuan Wang: set up measurement system, data curation, writing the original draft. Yiming Lin: programming. Xiuyu Chen: English writing assistance. Qingshan Jiang: system integration guidance. Changcai Cui: conceptualization.

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Correspondence to Junying Chen.

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Chen, J., Wang, B., Lin, Y. et al. Efficient-Unet: Intelligent identification of abrasive grain on the entire surface of monolayer brazing wheel based on encoder–decoder network. Int J Adv Manuf Technol 131, 6027–6037 (2024). https://doi.org/10.1007/s00170-024-13305-4

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  • DOI: https://doi.org/10.1007/s00170-024-13305-4

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