A Method for Measurement of Workpiece form Deviations Based on Machine Vision
Machine vision has been studied for measurements of workpiece form deviations due to its ease of automation. However, the measurement accuracy limits its wide implementation in industrial applications. In this study, a method based on machine vision for measurement of straightness, roundness, and cy...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-08-01
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| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/10/8/718 |
| _version_ | 1827617803613503488 |
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| author | Wei Zhang Zongwang Han Yang Li Hongyu Zheng Xiang Cheng |
| author_facet | Wei Zhang Zongwang Han Yang Li Hongyu Zheng Xiang Cheng |
| author_sort | Wei Zhang |
| collection | DOAJ |
| description | Machine vision has been studied for measurements of workpiece form deviations due to its ease of automation. However, the measurement accuracy limits its wide implementation in industrial applications. In this study, a method based on machine vision for measurement of straightness, roundness, and cylindricity of a workpiece is presented. A subsumed line search algorithm and an improved particle swarm optimization algorithm are proposed to evaluate the straightness and roundness deviations of the workpiece. Moreover, an image evaluation method of cylindricity deviation by the least-square fitting of the circle’s center coordinates is investigated. An image acquisition system incorporating image correction and sub-pixel edge positioning technology is developed. The performance of the developed system is evaluated against the measurement results of the standard cylindricity measuring instrument. The differences in the measurement of straightness, roundness, and cylindricity are −4.69 μm, 3.87 μm, and 8.51 μm, respectively. The proposed method would provide a viable industrial solution for the measurement of workpiece form deviations. |
| first_indexed | 2024-03-09T09:54:35Z |
| format | Article |
| id | doaj.art-aa3dee39ac0e4e568be24461d0d744d6 |
| institution | Directory Open Access Journal |
| issn | 2075-1702 |
| language | English |
| last_indexed | 2024-03-09T09:54:35Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj.art-aa3dee39ac0e4e568be24461d0d744d62023-12-01T23:55:44ZengMDPI AGMachines2075-17022022-08-0110871810.3390/machines10080718A Method for Measurement of Workpiece form Deviations Based on Machine VisionWei Zhang0Zongwang Han1Yang Li2Hongyu Zheng3Xiang Cheng4School of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaSchool of Mechanical Engineering, Shandong University of Technology, Zibo 255000, ChinaMachine vision has been studied for measurements of workpiece form deviations due to its ease of automation. However, the measurement accuracy limits its wide implementation in industrial applications. In this study, a method based on machine vision for measurement of straightness, roundness, and cylindricity of a workpiece is presented. A subsumed line search algorithm and an improved particle swarm optimization algorithm are proposed to evaluate the straightness and roundness deviations of the workpiece. Moreover, an image evaluation method of cylindricity deviation by the least-square fitting of the circle’s center coordinates is investigated. An image acquisition system incorporating image correction and sub-pixel edge positioning technology is developed. The performance of the developed system is evaluated against the measurement results of the standard cylindricity measuring instrument. The differences in the measurement of straightness, roundness, and cylindricity are −4.69 μm, 3.87 μm, and 8.51 μm, respectively. The proposed method would provide a viable industrial solution for the measurement of workpiece form deviations.https://www.mdpi.com/2075-1702/10/8/718machine visionform deviationevaluation algorithmimage-based processedge detection |
| spellingShingle | Wei Zhang Zongwang Han Yang Li Hongyu Zheng Xiang Cheng A Method for Measurement of Workpiece form Deviations Based on Machine Vision Machines machine vision form deviation evaluation algorithm image-based process edge detection |
| title | A Method for Measurement of Workpiece form Deviations Based on Machine Vision |
| title_full | A Method for Measurement of Workpiece form Deviations Based on Machine Vision |
| title_fullStr | A Method for Measurement of Workpiece form Deviations Based on Machine Vision |
| title_full_unstemmed | A Method for Measurement of Workpiece form Deviations Based on Machine Vision |
| title_short | A Method for Measurement of Workpiece form Deviations Based on Machine Vision |
| title_sort | method for measurement of workpiece form deviations based on machine vision |
| topic | machine vision form deviation evaluation algorithm image-based process edge detection |
| url | https://www.mdpi.com/2075-1702/10/8/718 |
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