{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T12:12:27Z","timestamp":1775477547123,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2016,8,25]],"date-time":"2016-08-25T00:00:00Z","timestamp":1472083200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Surface defect detection and dimension measurement of automotive bevel gears by manual inspection are costly, inefficient, low speed and low accuracy. In order to solve these problems, a synthetic bevel gear quality inspection system based on multi-camera vision technology is developed. The system can detect surface defects and measure gear dimensions simultaneously. Three efficient algorithms named Neighborhood Average Difference (NAD), Circle Approximation Method (CAM) and Fast Rotation-Position (FRP) are proposed. The system can detect knock damage, cracks, scratches, dents, gibbosity or repeated cutting of the spline, etc. The smallest detectable defect is 0.4 mm \u00d7 0.4 mm and the precision of dimension measurement is about 40\u201350 \u03bcm. One inspection process takes no more than 1.3 s. Both precision and speed meet the requirements of real-time online inspection in bevel gear production.<\/jats:p>","DOI":"10.3390\/s16091364","type":"journal-article","created":{"date-parts":[[2016,8,25]],"date-time":"2016-08-25T10:11:44Z","timestamp":1472119904000},"page":"1364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0107-6352","authenticated-orcid":false,"given":"Ruiling","family":"Liu","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6806-6300","authenticated-orcid":false,"given":"Dexing","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Hongqiang","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]},{"given":"Jiuqiang","family":"Han","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}]}],"member":"1968","published-online":{"date-parts":[[2016,8,25]]},"reference":[{"key":"ref_1","first-page":"613","article-title":"Fault prognosis and diagnosis of an automotive rear axle gear using a RBF-BP neural network","volume":"Volume 305","author":"Ouyang","year":"2011","journal-title":"9th International Conference on Damage Assessment of Structures"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.ymssp.2005.12.011","article-title":"Impact velocity modelling and signal processing of spur gear vibration for the estimation of defect size","volume":"21","author":"Parey","year":"2007","journal-title":"Mech. 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