Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method
A gear integrated error, a combination of individual and composite errors, carries richer information and has long been a key target of classic gear error measurement techniques. However, in the age of intelligent manufacturing, the classic methods for gear integrated error measurement are no longer...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/3/1004 |
| _version_ | 1797319077167890432 |
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| author | Yiming Fang Zhaoyao Shi Yanqiang Sun Pan Zhang |
| author_facet | Yiming Fang Zhaoyao Shi Yanqiang Sun Pan Zhang |
| author_sort | Yiming Fang |
| collection | DOAJ |
| description | A gear integrated error, a combination of individual and composite errors, carries richer information and has long been a key target of classic gear error measurement techniques. However, in the age of intelligent manufacturing, the classic methods for gear integrated error measurement are no longer able to meet the emerging requirements of large-scale gears and real-time online measurement. To address this gap, a novel approach to obtaining the gear integrated error based on GTC−Facet (Gaussian template convolution-Facet) is proposed. This method accurately pinpoints the sub-pixel contour of gears in images, enabling a quick derivation of the gear integrated error curve. From this curve, other individual and composite errors can be analyzed. The gear error information obtained through our method has higher measurement accuracy, achieving a positioning accuracy of 3.6 μm for the gear profile. Moreover, during the measurement process, the measured gear remains unclamped, and the entire measurement process can be completed within 0.35 s, which is much faster than classic methods. Our method meets the demands of online measurements and provides a new avenue for gear error measurement. |
| first_indexed | 2024-03-08T04:01:40Z |
| format | Article |
| id | doaj.art-730cbbcbd18242cb8175bcbe773a8e3c |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T04:01:40Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-730cbbcbd18242cb8175bcbe773a8e3c2024-02-09T15:07:17ZengMDPI AGApplied Sciences2076-34172024-01-01143100410.3390/app14031004Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet MethodYiming Fang0Zhaoyao Shi1Yanqiang Sun2Pan Zhang3Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaBeijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaSchool of Construction Machinery, Shandong Jiaotong University, Jinan 250357, ChinaBeijing Engineering Research Center of Precision Measurement Technology and Instruments, Faculty of Materials and Manufacturing, Beijing University of Technology, Beijing 100124, ChinaA gear integrated error, a combination of individual and composite errors, carries richer information and has long been a key target of classic gear error measurement techniques. However, in the age of intelligent manufacturing, the classic methods for gear integrated error measurement are no longer able to meet the emerging requirements of large-scale gears and real-time online measurement. To address this gap, a novel approach to obtaining the gear integrated error based on GTC−Facet (Gaussian template convolution-Facet) is proposed. This method accurately pinpoints the sub-pixel contour of gears in images, enabling a quick derivation of the gear integrated error curve. From this curve, other individual and composite errors can be analyzed. The gear error information obtained through our method has higher measurement accuracy, achieving a positioning accuracy of 3.6 μm for the gear profile. Moreover, during the measurement process, the measured gear remains unclamped, and the entire measurement process can be completed within 0.35 s, which is much faster than classic methods. Our method meets the demands of online measurements and provides a new avenue for gear error measurement.https://www.mdpi.com/2076-3417/14/3/1004Canny operatorgear integrated errorGaussian convolutionmachine visionsub-pixel |
| spellingShingle | Yiming Fang Zhaoyao Shi Yanqiang Sun Pan Zhang Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method Applied Sciences Canny operator gear integrated error Gaussian convolution machine vision sub-pixel |
| title | Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method |
| title_full | Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method |
| title_fullStr | Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method |
| title_full_unstemmed | Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method |
| title_short | Gear Integrated Error Determination Using the Gaussian Template Convolution-Facet Method |
| title_sort | gear integrated error determination using the gaussian template convolution facet method |
| topic | Canny operator gear integrated error Gaussian convolution machine vision sub-pixel |
| url | https://www.mdpi.com/2076-3417/14/3/1004 |
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