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A novel parallel classification network for classifying three-dimensional surface with point cloud data

Author

Listed:
  • Chen Zhao

    (Shanghai Jiao Tong University)

  • Shichang Du

    (Shanghai Jiao Tong University)

  • Jun Lv

    (East China Normal University)

  • Yafei Deng

    (Shanghai Jiao Tong University)

  • Guilong Li

    (Shanghai Jiao Tong University)

Abstract

Surface classification is an effective way to assess the surface quality of parts. During the last decade, the assessment of parts quality has gradually changed from simple geometries to complex three-dimensional (3D) surfaces. Traditional quality assessment methods rely on identifying key product characteristics of parts, e.g., the profile of surface. However, for point cloud data obtained by high-definition metrology, traditional methods cannot make full use of the data and lose a lot of information. This paper proposes a systematic approach for classifying the quality of 3D surfaces based on point cloud data. Firstly, point clouds of different samples are registered to the same coordinate system by point cloud registration. Secondly, the point cloud is divided into several sub-regions by fuzzy clustering. Finally, a novel parallel classification network method based on deep learning is proposed to directly process point cloud data and classify 3D surfaces. The performance of the proposed method is evaluated through simulation and an actual case study of the combustion chamber surfaces of the engine cylinder heads. The results show that the proposed method can significantly improve the classification accuracy of 3D surfaces based on point cloud data.

Suggested Citation

  • Chen Zhao & Shichang Du & Jun Lv & Yafei Deng & Guilong Li, 2023. "A novel parallel classification network for classifying three-dimensional surface with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 515-527, February.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:2:d:10.1007_s10845-021-01802-2
    DOI: 10.1007/s10845-021-01802-2
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    References listed on IDEAS

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    1. Linmiao Zhang & Kaibo Wang & Nan Chen, 2016. "Monitoring wafers’ geometric quality using an additive Gaussian process model," IISE Transactions, Taylor & Francis Journals, vol. 48(1), pages 1-15, January.
    2. Lee J. Wells & Romina Dastoorian & Jaime A. Camelio, 2021. "A novel NURBS surface approach to statistically monitor manufacturing processes with point cloud data," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 329-345, February.
    3. Fangwei Ning & Yan Shi & Maolin Cai & Weiqing Xu, 2020. "Various realization methods of machine-part classification based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 2019-2032, December.
    4. Sue E. Stankus & Krystel K. Castillo-Villar, 2019. "An Improved multivariate generalised likelihood ratio control chart for the monitoring of point clouds from 3D laser scanners," International Journal of Production Research, Taylor & Francis Journals, vol. 57(8), pages 2344-2355, April.
    5. Xiang Li & Wei Zhang & Qian Ding & Jian-Qiao Sun, 2020. "Intelligent rotating machinery fault diagnosis based on deep learning using data augmentation," Journal of Intelligent Manufacturing, Springer, vol. 31(2), pages 433-452, February.
    Full references (including those not matched with items on IDEAS)

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