Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts
In the high-stakes domain of precision manufacturing, Cubic Boron Nitride (CBN) inserts are pivotal for their hardness and durability. However, post-production surface defects on these inserts can compromise product integrity and performance. This paper proposes an automated detection and classifica...
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MDPI AG
2024-12-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/23/7824 |
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| author | Li Zeng Feng Wan Baiyun Zhang Xu Zhu |
| author_facet | Li Zeng Feng Wan Baiyun Zhang Xu Zhu |
| author_sort | Li Zeng |
| collection | DOAJ |
| description | In the high-stakes domain of precision manufacturing, Cubic Boron Nitride (CBN) inserts are pivotal for their hardness and durability. However, post-production surface defects on these inserts can compromise product integrity and performance. This paper proposes an automated detection and classification system using machine vision to scrutinize these surface defects. By integrating an optical bracket, a high-resolution industrial camera, precise lighting, and an advanced development board, the system employs digital image processing to ascertain and categorize imperfections on CBN inserts. The methodology initiates with a high-definition image capture by the imaging platform, tailored for CBN insert inspection. A suite of defect detection algorithms undergoes comparative analysis to discern their efficacy, emphasizing the impact of algorithm parameters and dataset diversity on detection precision. The most effective algorithm is then encapsulated into a versatile application, ensuring compatibility with various operating systems. Empirical verification of the system shows that the detection accuracy of multiple defect types exceeds 90%, and the tooth surface recognition efficiency significantly reaches three frames per second, with the front and side cutting surfaces of the tool in each frame. This breakthrough indicates a scalable, reliable solution for automatically detecting and classifying surface defects on CBN inserts, paving the way for enhanced quality control in automated, high-speed production lines. |
| format | Article |
| id | doaj-art-2b6177ebda0f4bad8fdd74dda87d1315 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-2b6177ebda0f4bad8fdd74dda87d13152025-08-20T01:55:31ZengMDPI AGSensors1424-82202024-12-012423782410.3390/s24237824Automated Visual Inspection for Precise Defect Detection and Classification in CBN InsertsLi Zeng0Feng Wan1Baiyun Zhang2Xu Zhu3School of Mechanical and Electrical Engineering, Zhejiang Industry Polytechnic College, Shaoxing 312000, ChinaNingbo Jiapeng Machinery Equipment Manufacturing Co., Ltd., Ningbo 315101, ChinaNingbo Institute of Dalian University of Technology, Ningbo 315032, ChinaNingbo Institute of Dalian University of Technology, Ningbo 315032, ChinaIn the high-stakes domain of precision manufacturing, Cubic Boron Nitride (CBN) inserts are pivotal for their hardness and durability. However, post-production surface defects on these inserts can compromise product integrity and performance. This paper proposes an automated detection and classification system using machine vision to scrutinize these surface defects. By integrating an optical bracket, a high-resolution industrial camera, precise lighting, and an advanced development board, the system employs digital image processing to ascertain and categorize imperfections on CBN inserts. The methodology initiates with a high-definition image capture by the imaging platform, tailored for CBN insert inspection. A suite of defect detection algorithms undergoes comparative analysis to discern their efficacy, emphasizing the impact of algorithm parameters and dataset diversity on detection precision. The most effective algorithm is then encapsulated into a versatile application, ensuring compatibility with various operating systems. Empirical verification of the system shows that the detection accuracy of multiple defect types exceeds 90%, and the tooth surface recognition efficiency significantly reaches three frames per second, with the front and side cutting surfaces of the tool in each frame. This breakthrough indicates a scalable, reliable solution for automatically detecting and classifying surface defects on CBN inserts, paving the way for enhanced quality control in automated, high-speed production lines.https://www.mdpi.com/1424-8220/24/23/7824CBN insertdefect detectionvisual inspection technologydeep learning |
| spellingShingle | Li Zeng Feng Wan Baiyun Zhang Xu Zhu Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts Sensors CBN insert defect detection visual inspection technology deep learning |
| title | Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts |
| title_full | Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts |
| title_fullStr | Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts |
| title_full_unstemmed | Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts |
| title_short | Automated Visual Inspection for Precise Defect Detection and Classification in CBN Inserts |
| title_sort | automated visual inspection for precise defect detection and classification in cbn inserts |
| topic | CBN insert defect detection visual inspection technology deep learning |
| url | https://www.mdpi.com/1424-8220/24/23/7824 |
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