Defect detection of gear parts in virtual manufacturing
Abstract 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 representatio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-03-01
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| Series: | Visual Computing for Industry, Biomedicine, and Art |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s42492-023-00133-8 |
| _version_ | 1827974868740603904 |
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| author | Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao |
| author_facet | Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao |
| author_sort | Zhenxing Xu |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-09T20:01:20Z |
| format | Article |
| id | doaj.art-db7122afc9874b08b79beff0c02bc4ab |
| institution | Directory Open Access Journal |
| issn | 2524-4442 |
| language | English |
| last_indexed | 2024-04-09T20:01:20Z |
| publishDate | 2023-03-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Visual Computing for Industry, Biomedicine, and Art |
| spelling | doaj.art-db7122afc9874b08b79beff0c02bc4ab2023-04-03T05:15:19ZengSpringerOpenVisual Computing for Industry, Biomedicine, and Art2524-44422023-03-016111210.1186/s42492-023-00133-8Defect detection of gear parts in virtual manufacturingZhenxing Xu0Aizeng Wang1Fei Hou2Gang Zhao3School of Mechanical Engineering & Automation, Beihang UniversitySchool of Mechanical Engineering & Automation, Beihang UniversityState Key Laboratory of Computer Science, Institute of Software, Chinese Academy of SciencesSchool of Mechanical Engineering & Automation, Beihang UniversityAbstract 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.https://doi.org/10.1186/s42492-023-00133-8Defect detectionGear surfaceGear datasetCombinational Convolution Block |
| spellingShingle | Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao Defect detection of gear parts in virtual manufacturing Visual Computing for Industry, Biomedicine, and Art Defect detection Gear surface Gear dataset Combinational Convolution Block |
| title | Defect detection of gear parts in virtual manufacturing |
| title_full | Defect detection of gear parts in virtual manufacturing |
| title_fullStr | Defect detection of gear parts in virtual manufacturing |
| title_full_unstemmed | Defect detection of gear parts in virtual manufacturing |
| title_short | Defect detection of gear parts in virtual manufacturing |
| title_sort | defect detection of gear parts in virtual manufacturing |
| topic | Defect detection Gear surface Gear dataset Combinational Convolution Block |
| url | https://doi.org/10.1186/s42492-023-00133-8 |
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