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Conference Paper: Automated Visual Inspection of Metallic Parts

TitleAutomated Visual Inspection of Metallic Parts
Authors
Issue Date2017
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800486
Citation
Proceedings of 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Honolulu, HI, USA, 31 July-4 August 2017, p. 1158-1162 How to Cite?
AbstractThe copper alloy casting parts are widely used in the manufacturing of the taps, pipes, motor stators, crafts, and so on. It is required to recognize the casting parts which have the surface blisters during the on-line automatic grinding. In this paper, we propose a high-speed automatic visual inspection system to detect the surface blisters in real time. The casting part is mounted on the manipulator that can automatically adjust the posture of the casting part in the visual inspection system. The images of casting parts are captured by a single high speed industrial camera, then the feature analysis is applied to these images to identify the blisters. The contour and intensity are extracted to describe the surface blister features which are used to train the recognition model. In the experiments, copper alloy taps are inspected after grinding. A KUKA robotic arm is used to move a part to different inspection surfaces, and an Ethernet camera is used to capture the images. It takes about 35ms for processing one captured frame. For the whole casting taps, it takes less than 10s to scan the whole surface. The results show that the failure rate is nearly 0% and false alarm rate is under 2% for each surface blister.
Persistent Identifierhttp://hdl.handle.net/10722/261967
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLiu, J-
dc.contributor.authorLi, Y-
dc.contributor.authorPan, C-
dc.contributor.authorChen, H-
dc.contributor.authorXi, N-
dc.date.accessioned2018-09-28T04:51:06Z-
dc.date.available2018-09-28T04:51:06Z-
dc.date.issued2017-
dc.identifier.citationProceedings of 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Honolulu, HI, USA, 31 July-4 August 2017, p. 1158-1162-
dc.identifier.isbn9781538604908-
dc.identifier.urihttp://hdl.handle.net/10722/261967-
dc.description.abstractThe copper alloy casting parts are widely used in the manufacturing of the taps, pipes, motor stators, crafts, and so on. It is required to recognize the casting parts which have the surface blisters during the on-line automatic grinding. In this paper, we propose a high-speed automatic visual inspection system to detect the surface blisters in real time. The casting part is mounted on the manipulator that can automatically adjust the posture of the casting part in the visual inspection system. The images of casting parts are captured by a single high speed industrial camera, then the feature analysis is applied to these images to identify the blisters. The contour and intensity are extracted to describe the surface blister features which are used to train the recognition model. In the experiments, copper alloy taps are inspected after grinding. A KUKA robotic arm is used to move a part to different inspection surfaces, and an Ethernet camera is used to capture the images. It takes about 35ms for processing one captured frame. For the whole casting taps, it takes less than 10s to scan the whole surface. The results show that the failure rate is nearly 0% and false alarm rate is under 2% for each surface blister.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800486-
dc.relation.ispartofIEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER)-
dc.rightsIEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). Copyright © IEEE.-
dc.titleAutomated Visual Inspection of Metallic Parts-
dc.typeConference_Paper-
dc.identifier.emailXi, N: xining@hku.hk-
dc.identifier.authorityXi, N=rp02044-
dc.identifier.doi10.1109/CYBER.2017.8446519-
dc.identifier.hkuros292778-
dc.identifier.spage1158-
dc.identifier.epage1162-
dc.publisher.placeUnited States-

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