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Art"],"abstract":"<jats:title>Abstract<\/jats:title><jats:p>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+\u2009+\u2009introduces 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.<\/jats:p>","DOI":"10.1186\/s42492-023-00133-8","type":"journal-article","created":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T08:06:29Z","timestamp":1680077189000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Defect detection of gear parts in virtual manufacturing"],"prefix":"10.1186","volume":"6","author":[{"given":"Zhenxing","family":"Xu","sequence":"first","affiliation":[]},{"given":"Aizeng","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Hou","sequence":"additional","affiliation":[]},{"given":"Gang","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"issue":"4","key":"133_CR1","first-page":"243","volume":"6","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu QN, Wu X, Na J (2016) Gear fault diagnosis based on narrowband demodulation with frequency shift and spectrum edit. 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