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A machining feature recognition approach based on hierarchical neural network for multi-feature point cloud models

Author

Listed:
  • Xinhua Yao

    (Zhejiang University
    Zhejiang University)

  • Di Wang

    (Zhejiang University
    Zhejiang University)

  • Tao Yu

    (Zhejiang University
    Zhejiang University)

  • Congcong Luan

    (Zhejiang University
    Zhejiang University
    Zhejiang University)

  • Jianzhong Fu

    (Zhejiang University
    Zhejiang University)

Abstract

Most mechanical part models in current industrial manufacturing are composed of multiple different machining features. However, the traditional rule-based feature recognition methods are only suitable for analyzing simple and specific features. Although the existing methods based on deep learning are no longer limited to recognizing particular features, they cannot recognize complex overlapping features. To solve the above issues, this paper proposed a machining feature recognition approach based on the hierarchical neural network to recognize the multiple features on point cloud models. Firstly, the 3D models were converted into point cloud samples to construct the dataset, so that the approach could be applied to different 3D model formats. Then a hierarchical neural network called PointNet++ for single feature recognition was constructed. For the multi-feature point cloud models, a feature segmentation method was proposed to divide a complex multi-feature model into single feature models for recognition. Finally, the approach was evaluated on the created test data sets. The test results show that the overlapping machining feature on point cloud models can be accurately recognized with low computational cost.

Suggested Citation

  • Xinhua Yao & Di Wang & Tao Yu & Congcong Luan & Jianzhong Fu, 2023. "A machining feature recognition approach based on hierarchical neural network for multi-feature point cloud models," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2599-2610, August.
    • Handle: RePEc:spr:joinma:v:34:y:2023:i:6:d:10.1007_s10845-022-01939-8
    DOI: 10.1007/s10845-022-01939-8
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    References listed on IDEAS

    as
    1. 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.
    2. Peizhi Shi & Qunfen Qi & Yuchu Qin & Paul J. Scott & Xiangqian Jiang, 2020. "A novel learning-based feature recognition method using multiple sectional view representation," Journal of Intelligent Manufacturing, Springer, vol. 31(5), pages 1291-1309, June.
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