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A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology

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 c...

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Detalles Bibliográficos
Autores principales: Liu, Ruiling, Zhong, Dexing, Lyu, Hongqiang, Han, Jiuqiang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038642/
https://www.ncbi.nlm.nih.gov/pubmed/27571078
http://dx.doi.org/10.3390/s16091364
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author Liu, Ruiling
Zhong, Dexing
Lyu, Hongqiang
Han, Jiuqiang
author_facet Liu, Ruiling
Zhong, Dexing
Lyu, Hongqiang
Han, Jiuqiang
author_sort Liu, Ruiling
collection PubMed
description 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 × 0.4 mm and the precision of dimension measurement is about 40–50 μm. 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.
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spelling pubmed-50386422016-09-29 A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology Liu, Ruiling Zhong, Dexing Lyu, Hongqiang Han, Jiuqiang Sensors (Basel) Article 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 × 0.4 mm and the precision of dimension measurement is about 40–50 μm. 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. MDPI 2016-08-25 /pmc/articles/PMC5038642/ /pubmed/27571078 http://dx.doi.org/10.3390/s16091364 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Liu, Ruiling
Zhong, Dexing
Lyu, Hongqiang
Han, Jiuqiang
A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology
title A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology
title_full A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology
title_fullStr A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology
title_full_unstemmed A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology
title_short A Bevel Gear Quality Inspection System Based on Multi-Camera Vision Technology
title_sort bevel gear quality inspection system based on multi-camera vision technology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5038642/
https://www.ncbi.nlm.nih.gov/pubmed/27571078
http://dx.doi.org/10.3390/s16091364
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