On-line monitoring of penetration state in laser-arc hybrid welding based on keyhole and arc features
Incomplete penetration is a typical defect in laser-arc hybrid welding. On-line monitoring of welding process is an important method to assess welding quality. In laser-arc hybrid welding, there is a strong correlation between keyhole, arc and incomplete penetration. Therefore, an on-line monitoring...
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| Veröffentlicht in: | Journal of physics. Conference series 2021-04, Vol.1884 (1), p.12039 |
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| container_title | Journal of physics. Conference series |
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| creator | Zhang, Minghai Shu, Leshi Zhou, Qi Jiang, Ping Gong, Zhaoliang |
| description | Incomplete penetration is a typical defect in laser-arc hybrid welding. On-line monitoring of welding process is an important method to assess welding quality. In laser-arc hybrid welding, there is a strong correlation between keyhole, arc and incomplete penetration. Therefore, an on-line monitoring method of penetration state based on keyhole and arc features is proposed in this paper. In the proposed method, the images of keyhole and arc in laser-arc hybrid welding are captured by high-speed camera, and then the keyhole and arc features are extracted by image processing algorithm. Finally, the features are used as input to classify the penetration state using SVM model. The results show that the SVM model based on keyhole and arc features can accurately identify the penetration state. |
| doi_str_mv | 10.1088/1742-6596/1884/1/012039 |
| format | Article |
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On-line monitoring of welding process is an important method to assess welding quality. In laser-arc hybrid welding, there is a strong correlation between keyhole, arc and incomplete penetration. Therefore, an on-line monitoring method of penetration state based on keyhole and arc features is proposed in this paper. In the proposed method, the images of keyhole and arc in laser-arc hybrid welding are captured by high-speed camera, and then the keyhole and arc features are extracted by image processing algorithm. Finally, the features are used as input to classify the penetration state using SVM model. 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Conference series</title><description>Incomplete penetration is a typical defect in laser-arc hybrid welding. On-line monitoring of welding process is an important method to assess welding quality. In laser-arc hybrid welding, there is a strong correlation between keyhole, arc and incomplete penetration. Therefore, an on-line monitoring method of penetration state based on keyhole and arc features is proposed in this paper. In the proposed method, the images of keyhole and arc in laser-arc hybrid welding are captured by high-speed camera, and then the keyhole and arc features are extracted by image processing algorithm. Finally, the features are used as input to classify the penetration state using SVM model. The results show that the SVM model based on keyhole and arc features can accurately identify the penetration state.</description><subject>Algorithms</subject><subject>Arc welding</subject><subject>Feature extraction</subject><subject>High speed cameras</subject><subject>Image classification</subject><subject>Image processing</subject><subject>Laser beam welding</subject><subject>Lasers</subject><subject>Monitoring</subject><subject>Penetration</subject><subject>Physics</subject><subject>Quality assessment</subject><issn>1742-6588</issn><issn>1742-6596</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNo9kE1LAzEQhoMoWKu_wYDndZNstkmOUvyCQi96DtnNxKZuk5qkSP-9u1Q6lxl4n5mBB6F7Sh4pkbKmgrNq0apFTaXkNa0JZaRRF2h2Ti7Ps5TX6CbnLSHNWGKGYB2qwQfAuxh8icmHLxwd3kOAkkzxMeBcTAHsAx5MhlSZ1OPNsUve4l8Y7LTQjYHFI_oNx00cAJtg8cQ5MOWQIN-iK2eGDHf_fY4-X54_lm_Vav36vnxaVT3jSlVUMgfOCc6dEoJCxxlrhLOu6QUloKRyrl8QYLxTiljHuek54VzSXggraTNHD6e7-xR_DpCL3sZDCuNLzVoqx5tt24yUOFF9ijkncHqf_M6ko6ZET071ZEtP5vTkVFN9ctr8Aavnals</recordid><startdate>20210401</startdate><enddate>20210401</enddate><creator>Zhang, Minghai</creator><creator>Shu, Leshi</creator><creator>Zhou, Qi</creator><creator>Jiang, Ping</creator><creator>Gong, Zhaoliang</creator><general>IOP Publishing</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>H8D</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20210401</creationdate><title>On-line monitoring of penetration state in laser-arc hybrid welding based on keyhole and arc features</title><author>Zhang, Minghai ; Shu, Leshi ; Zhou, Qi ; Jiang, Ping ; Gong, Zhaoliang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2499-182feff744f9771eb42237fdf3c710e989ffc60e24b990df44ac404481c77d813</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Arc welding</topic><topic>Feature extraction</topic><topic>High speed cameras</topic><topic>Image classification</topic><topic>Image processing</topic><topic>Laser beam welding</topic><topic>Lasers</topic><topic>Monitoring</topic><topic>Penetration</topic><topic>Physics</topic><topic>Quality assessment</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Minghai</creatorcontrib><creatorcontrib>Shu, Leshi</creatorcontrib><creatorcontrib>Zhou, Qi</creatorcontrib><creatorcontrib>Jiang, Ping</creatorcontrib><creatorcontrib>Gong, Zhaoliang</creatorcontrib><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Aerospace Database</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Journal of physics. Conference series</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhang, Minghai</au><au>Shu, Leshi</au><au>Zhou, Qi</au><au>Jiang, Ping</au><au>Gong, Zhaoliang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On-line monitoring of penetration state in laser-arc hybrid welding based on keyhole and arc features</atitle><jtitle>Journal of physics. Conference series</jtitle><date>2021-04-01</date><risdate>2021</risdate><volume>1884</volume><issue>1</issue><spage>12039</spage><pages>12039-</pages><issn>1742-6588</issn><eissn>1742-6596</eissn><abstract>Incomplete penetration is a typical defect in laser-arc hybrid welding. On-line monitoring of welding process is an important method to assess welding quality. In laser-arc hybrid welding, there is a strong correlation between keyhole, arc and incomplete penetration. Therefore, an on-line monitoring method of penetration state based on keyhole and arc features is proposed in this paper. In the proposed method, the images of keyhole and arc in laser-arc hybrid welding are captured by high-speed camera, and then the keyhole and arc features are extracted by image processing algorithm. Finally, the features are used as input to classify the penetration state using SVM model. The results show that the SVM model based on keyhole and arc features can accurately identify the penetration state.</abstract><cop>Bristol</cop><pub>IOP Publishing</pub><doi>10.1088/1742-6596/1884/1/012039</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Arc welding Feature extraction High speed cameras Image classification Image processing Laser beam welding Lasers Monitoring Penetration Physics Quality assessment |
| title | On-line monitoring of penetration state in laser-arc hybrid welding based on keyhole and arc features |
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