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Various realization methods of machine-part classification based on deep learning

Author

Listed:
  • Fangwei Ning

    (Beihang University)

  • Yan Shi

    (Beihang University)

  • Maolin Cai

    (Beihang University)

  • Weiqing Xu

    (Beihang University)

Abstract

Parts classification can improve the efficacy of the manufacturing process in a computer-aided process planning system. In this study, we investigate various methodologies to assist with parts classification based on deep learning technologies, including a two-dimensional convolutional neural network (2D-CNN) trained using both picture data and CSV files; and a three-dimensional convolutional neural network (3D-CNN) trained using voxel data. Additionally, two novel methods are proposed: (1) feature recognition for the processing parts based on syntactic patterns, where their feature quantities are computed and saved to comma-separated variable (CSV) files that are subsequently employed to train the 2D-CNN model; and (2) voxelization of parts, wherein the voxel data of the parts is obtained for training the 3D-CNN model. The two methods are compared with a 2D-CNN model trained with the images of parts to classify. Results indicated that the 2D-CNN model trained with CSV data yielded the best performance and highest accuracy, followed by the 3D-CNN model, which was simpler and easier to implement and utilized better learning ability for the parts’ details. The 2D-CNN model trained with picture files evinced the lowest accuracy and a complex training network.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:joinma:v:31:y:2020:i:8:d:10.1007_s10845-020-01550-9
    DOI: 10.1007/s10845-020-01550-9
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    References listed on IDEAS

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    1. Jinfeng Liu & Xiaojun Liu & Zhonghua Ni & Honggen Zhou, 2018. "A new method of reusing the manufacturing information for the slightly changed 3D CAD model," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1827-1844, December.
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    Cited by:

    1. 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.
    2. Minyoung Lee & Joohyoung Jeon & Hongchul Lee, 2022. "Explainable AI for domain experts: a post Hoc analysis of deep learning for defect classification of TFT–LCD panels," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1747-1759, August.
    3. 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.

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