Gears play an important role in virtual manufacturing systems for digital twins; however, the image of gear tooth defects is difficult to acquire owing to its non-convex shape. In this study, a deep learning network is proposed to detect gear defects based on their point cloud representation. This approach mainly consists of three steps: (1) Various types of gear defects are classified into four cases (fracture, pitting, glue, and wear); A 3D gear dataset was constructed with 10000 instances following the aforementioned classification. (2) Gear-PCNet+ + introduces a novel Combinational Convolution Block, proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology; (3) Compared with other methods, experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.
CITATION STYLE
Xu, Z., Wang, A., Hou, F., & Zhao, G. (2023). Defect detection of gear parts in virtual manufacturing. Visual Computing for Industry, Biomedicine, and Art, 6(1). https://doi.org/10.1186/s42492-023-00133-8
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