Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors
In order to explore the relationship between the welding process and welded quality, a multiple sensor fusion system was built to obtain the photodiode and visible light information during the welding. Features of keyhole, plasma and spatters were extracted from five sensors, including two photodiod...
Gespeichert in:
| Veröffentlicht in: | Journal of intelligent manufacturing 2019-02, Vol.30 (2), p.821-832 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 832 |
|---|---|
| container_issue | 2 |
| container_start_page | 821 |
| container_title | Journal of intelligent manufacturing |
| container_volume | 30 |
| creator | Liu, Guiqian Gao, Xiangdong You, Deyong Zhang, Nanfeng |
| description | In order to explore the relationship between the welding process and welded quality, a multiple sensor fusion system was built to obtain the photodiode and visible light information during the welding. Features of keyhole, plasma and spatters were extracted from five sensors, including two photodiode sensors, one spectrometer sensor, one ultraviolet and visible light sensing camera and one auxiliary illumination sensing camera, 15 features were analyzed by normalization and principle component analysis, and principle component numbers was chosen as input parameters of support vector machine classification, Three weld quality types were defined according to the weld seam width and weld depth. The overall accuracy of training data was 98%, and the overall accuracy of testing data was 91%, respectively. Experimental results showed that the estimation on welding status was accurate and effective, thus providing an experimental example of monitoring high-power disk laser welding quality. |
| doi_str_mv | 10.1007/s10845-016-1286-y |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2183043530</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2183043530</sourcerecordid><originalsourceid>FETCH-LOGICAL-c430t-2489913cc8596a5aa0c3abbffb9e3f3531fc93e5b9b733e200e1245e9f1073323</originalsourceid><addsrcrecordid>eNp1kF1LwzAUhoMoOKc_wLuA19GTpOmayzH8gokDP25DmiZbRtfWpGXs35tRxStvzoHD874HHoSuKdxSgNldpFBkggDNCWVFTg4naELFjJGCZuIUTUCKnAhBxTm6iHELALLI6QTZVbCVN71vG9w6vPHrDe7avQ241jHNva0r36xx7HU_RFymY4UTu1rMsW4q_Pb5gk1Co3fe6N-a3VD3vqstjraJbYiX6MzpOtqrnz1FHw_374snsnx9fF7Ml8RkHHrCskJKyo0phMy10BoM12XpXCktd1xw6ozkVpSynHFuGYClLBNWOgrpwPgU3Yy9XWi_Bht7tW2H0KSXitGCQ5Y6IFF0pExoYwzWqS74nQ4HRUEdbarRpko21dGmOqQMGzMxsc3ahr_m_0Pf9Z14MA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2183043530</pqid></control><display><type>article</type><title>Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors</title><source>SpringerLink Journals - AutoHoldings</source><creator>Liu, Guiqian ; Gao, Xiangdong ; You, Deyong ; Zhang, Nanfeng</creator><creatorcontrib>Liu, Guiqian ; Gao, Xiangdong ; You, Deyong ; Zhang, Nanfeng</creatorcontrib><description>In order to explore the relationship between the welding process and welded quality, a multiple sensor fusion system was built to obtain the photodiode and visible light information during the welding. Features of keyhole, plasma and spatters were extracted from five sensors, including two photodiode sensors, one spectrometer sensor, one ultraviolet and visible light sensing camera and one auxiliary illumination sensing camera, 15 features were analyzed by normalization and principle component analysis, and principle component numbers was chosen as input parameters of support vector machine classification, Three weld quality types were defined according to the weld seam width and weld depth. The overall accuracy of training data was 98%, and the overall accuracy of testing data was 91%, respectively. Experimental results showed that the estimation on welding status was accurate and effective, thus providing an experimental example of monitoring high-power disk laser welding quality.</description><identifier>ISSN: 0956-5515</identifier><identifier>EISSN: 1572-8145</identifier><identifier>DOI: 10.1007/s10845-016-1286-y</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Business and Management ; Cameras ; Classification ; Control ; Feature extraction ; High power lasers ; Laser beam welding ; Light ; Machines ; Manufacturing ; Mechatronics ; Photodiodes ; Principal components analysis ; Processes ; Production ; Robotics ; Sensors ; Support vector machines ; Welding</subject><ispartof>Journal of intelligent manufacturing, 2019-02, Vol.30 (2), p.821-832</ispartof><rights>Springer Science+Business Media New York 2016</rights><rights>Journal of Intelligent Manufacturing is a copyright of Springer, (2016). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c430t-2489913cc8596a5aa0c3abbffb9e3f3531fc93e5b9b733e200e1245e9f1073323</citedby><cites>FETCH-LOGICAL-c430t-2489913cc8596a5aa0c3abbffb9e3f3531fc93e5b9b733e200e1245e9f1073323</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10845-016-1286-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10845-016-1286-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Liu, Guiqian</creatorcontrib><creatorcontrib>Gao, Xiangdong</creatorcontrib><creatorcontrib>You, Deyong</creatorcontrib><creatorcontrib>Zhang, Nanfeng</creatorcontrib><title>Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors</title><title>Journal of intelligent manufacturing</title><addtitle>J Intell Manuf</addtitle><description>In order to explore the relationship between the welding process and welded quality, a multiple sensor fusion system was built to obtain the photodiode and visible light information during the welding. Features of keyhole, plasma and spatters were extracted from five sensors, including two photodiode sensors, one spectrometer sensor, one ultraviolet and visible light sensing camera and one auxiliary illumination sensing camera, 15 features were analyzed by normalization and principle component analysis, and principle component numbers was chosen as input parameters of support vector machine classification, Three weld quality types were defined according to the weld seam width and weld depth. The overall accuracy of training data was 98%, and the overall accuracy of testing data was 91%, respectively. Experimental results showed that the estimation on welding status was accurate and effective, thus providing an experimental example of monitoring high-power disk laser welding quality.</description><subject>Business and Management</subject><subject>Cameras</subject><subject>Classification</subject><subject>Control</subject><subject>Feature extraction</subject><subject>High power lasers</subject><subject>Laser beam welding</subject><subject>Light</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mechatronics</subject><subject>Photodiodes</subject><subject>Principal components analysis</subject><subject>Processes</subject><subject>Production</subject><subject>Robotics</subject><subject>Sensors</subject><subject>Support vector machines</subject><subject>Welding</subject><issn>0956-5515</issn><issn>1572-8145</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kF1LwzAUhoMoOKc_wLuA19GTpOmayzH8gokDP25DmiZbRtfWpGXs35tRxStvzoHD874HHoSuKdxSgNldpFBkggDNCWVFTg4naELFjJGCZuIUTUCKnAhBxTm6iHELALLI6QTZVbCVN71vG9w6vPHrDe7avQ241jHNva0r36xx7HU_RFymY4UTu1rMsW4q_Pb5gk1Co3fe6N-a3VD3vqstjraJbYiX6MzpOtqrnz1FHw_374snsnx9fF7Ml8RkHHrCskJKyo0phMy10BoM12XpXCktd1xw6ozkVpSynHFuGYClLBNWOgrpwPgU3Yy9XWi_Bht7tW2H0KSXitGCQ5Y6IFF0pExoYwzWqS74nQ4HRUEdbarRpko21dGmOqQMGzMxsc3ahr_m_0Pf9Z14MA</recordid><startdate>20190201</startdate><enddate>20190201</enddate><creator>Liu, Guiqian</creator><creator>Gao, Xiangdong</creator><creator>You, Deyong</creator><creator>Zhang, Nanfeng</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>K9.</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M0S</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope></search><sort><creationdate>20190201</creationdate><title>Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors</title><author>Liu, Guiqian ; Gao, Xiangdong ; You, Deyong ; Zhang, Nanfeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c430t-2489913cc8596a5aa0c3abbffb9e3f3531fc93e5b9b733e200e1245e9f1073323</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Business and Management</topic><topic>Cameras</topic><topic>Classification</topic><topic>Control</topic><topic>Feature extraction</topic><topic>High power lasers</topic><topic>Laser beam welding</topic><topic>Light</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Mechatronics</topic><topic>Photodiodes</topic><topic>Principal components analysis</topic><topic>Processes</topic><topic>Production</topic><topic>Robotics</topic><topic>Sensors</topic><topic>Support vector machines</topic><topic>Welding</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Guiqian</creatorcontrib><creatorcontrib>Gao, Xiangdong</creatorcontrib><creatorcontrib>You, Deyong</creatorcontrib><creatorcontrib>Zhang, Nanfeng</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering 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>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><jtitle>Journal of intelligent manufacturing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Guiqian</au><au>Gao, Xiangdong</au><au>You, Deyong</au><au>Zhang, Nanfeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors</atitle><jtitle>Journal of intelligent manufacturing</jtitle><stitle>J Intell Manuf</stitle><date>2019-02-01</date><risdate>2019</risdate><volume>30</volume><issue>2</issue><spage>821</spage><epage>832</epage><pages>821-832</pages><issn>0956-5515</issn><eissn>1572-8145</eissn><abstract>In order to explore the relationship between the welding process and welded quality, a multiple sensor fusion system was built to obtain the photodiode and visible light information during the welding. Features of keyhole, plasma and spatters were extracted from five sensors, including two photodiode sensors, one spectrometer sensor, one ultraviolet and visible light sensing camera and one auxiliary illumination sensing camera, 15 features were analyzed by normalization and principle component analysis, and principle component numbers was chosen as input parameters of support vector machine classification, Three weld quality types were defined according to the weld seam width and weld depth. The overall accuracy of training data was 98%, and the overall accuracy of testing data was 91%, respectively. Experimental results showed that the estimation on welding status was accurate and effective, thus providing an experimental example of monitoring high-power disk laser welding quality.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10845-016-1286-y</doi><tpages>12</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0956-5515 |
| ispartof | Journal of intelligent manufacturing, 2019-02, Vol.30 (2), p.821-832 |
| issn | 0956-5515 1572-8145 |
| language | eng |
| recordid | cdi_proquest_journals_2183043530 |
| source | SpringerLink Journals - AutoHoldings |
| subjects | Business and Management Cameras Classification Control Feature extraction High power lasers Laser beam welding Light Machines Manufacturing Mechatronics Photodiodes Principal components analysis Processes Production Robotics Sensors Support vector machines Welding |
| title | Prediction of high power laser welding status based on PCA and SVM classification of multiple sensors |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-04T09%3A19%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Prediction%20of%20high%20power%20laser%20welding%20status%20based%20on%20PCA%20and%20SVM%20classification%20of%20multiple%20sensors&rft.jtitle=Journal%20of%20intelligent%20manufacturing&rft.au=Liu,%20Guiqian&rft.date=2019-02-01&rft.volume=30&rft.issue=2&rft.spage=821&rft.epage=832&rft.pages=821-832&rft.issn=0956-5515&rft.eissn=1572-8145&rft_id=info:doi/10.1007/s10845-016-1286-y&rft_dat=%3Cproquest_cross%3E2183043530%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2183043530&rft_id=info:pmid/&rfr_iscdi=true |