In-situ monitoring laser based directed energy deposition process with deep convolutional neural network
Laser based directed energy deposition (L-DED) is a promising type of additive manufacturing technology. The non-destructive testing technology for the quality monitoring of L-DED processed parts is becoming more and more demanding in terms of accuracy, real-time, and ease of operation. This paper i...
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| Veröffentlicht in: | Journal of intelligent manufacturing 2023-02, Vol.34 (2), p.683-693 |
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| container_title | Journal of intelligent manufacturing |
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| creator | Mi, Jiqian Zhang, Yikai Li, Hui Shen, Shengnan Yang, Yongqiang Song, Changhui Zhou, Xin Duan, Yucong Lu, Junwen Mai, Haibo |
| description | Laser based directed energy deposition (L-DED) is a promising type of additive manufacturing technology. The non-destructive testing technology for the quality monitoring of L-DED processed parts is becoming more and more demanding in terms of accuracy, real-time, and ease of operation. This paper introduces a new image recognition system based on a deep convolutional neural network, which uses multiple lightweight architectures to reduce detection time. In order to eliminate the interference better, it improves the penalty function, which effectively improves the accuracy. Judging from the detection results of the data set, the accuracy of the model training reaches 94.71%, which achieves a very good image segmentation effect and solves the technical problem of in-situ monitoring of the L-DED process. This system realizes the positioning of the spatters for the first time, and at the same time, the number of spatters and area of molten pool are correlated to the laser scanning speed and the laser power. |
| doi_str_mv | 10.1007/s10845-021-01820-0 |
| format | Article |
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The non-destructive testing technology for the quality monitoring of L-DED processed parts is becoming more and more demanding in terms of accuracy, real-time, and ease of operation. This paper introduces a new image recognition system based on a deep convolutional neural network, which uses multiple lightweight architectures to reduce detection time. In order to eliminate the interference better, it improves the penalty function, which effectively improves the accuracy. Judging from the detection results of the data set, the accuracy of the model training reaches 94.71%, which achieves a very good image segmentation effect and solves the technical problem of in-situ monitoring of the L-DED process. This system realizes the positioning of the spatters for the first time, and at the same time, the number of spatters and area of molten pool are correlated to the laser scanning speed and the laser power.</description><identifier>ISSN: 0956-5515</identifier><identifier>EISSN: 1572-8145</identifier><identifier>DOI: 10.1007/s10845-021-01820-0</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Accuracy ; Advanced manufacturing technologies ; Artificial neural networks ; Business and Management ; Control ; Deposition ; Image segmentation ; Laser applications ; Lasers ; Machines ; Manufacturing ; Mechatronics ; Model accuracy ; Monitoring ; Neural networks ; Nondestructive testing ; Object recognition ; Penalty function ; Processes ; Production ; Robotics</subject><ispartof>Journal of intelligent manufacturing, 2023-02, Vol.34 (2), p.683-693</ispartof><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c249t-ddc664fca19626c640dcb08ba260f4d2f9b1b0d986ba96e56a0c022a8bebff703</citedby><cites>FETCH-LOGICAL-c249t-ddc664fca19626c640dcb08ba260f4d2f9b1b0d986ba96e56a0c022a8bebff703</cites><orcidid>0000-0002-4404-8845</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10845-021-01820-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10845-021-01820-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,777,781,27905,27906,41469,42538,51300</link.rule.ids></links><search><creatorcontrib>Mi, Jiqian</creatorcontrib><creatorcontrib>Zhang, Yikai</creatorcontrib><creatorcontrib>Li, Hui</creatorcontrib><creatorcontrib>Shen, Shengnan</creatorcontrib><creatorcontrib>Yang, Yongqiang</creatorcontrib><creatorcontrib>Song, Changhui</creatorcontrib><creatorcontrib>Zhou, Xin</creatorcontrib><creatorcontrib>Duan, Yucong</creatorcontrib><creatorcontrib>Lu, Junwen</creatorcontrib><creatorcontrib>Mai, Haibo</creatorcontrib><title>In-situ monitoring laser based directed energy deposition process with deep convolutional neural network</title><title>Journal of intelligent manufacturing</title><addtitle>J Intell Manuf</addtitle><description>Laser based directed energy deposition (L-DED) is a promising type of additive manufacturing technology. The non-destructive testing technology for the quality monitoring of L-DED processed parts is becoming more and more demanding in terms of accuracy, real-time, and ease of operation. This paper introduces a new image recognition system based on a deep convolutional neural network, which uses multiple lightweight architectures to reduce detection time. In order to eliminate the interference better, it improves the penalty function, which effectively improves the accuracy. Judging from the detection results of the data set, the accuracy of the model training reaches 94.71%, which achieves a very good image segmentation effect and solves the technical problem of in-situ monitoring of the L-DED process. This system realizes the positioning of the spatters for the first time, and at the same time, the number of spatters and area of molten pool are correlated to the laser scanning speed and the laser power.</description><subject>Accuracy</subject><subject>Advanced manufacturing technologies</subject><subject>Artificial neural networks</subject><subject>Business and Management</subject><subject>Control</subject><subject>Deposition</subject><subject>Image segmentation</subject><subject>Laser applications</subject><subject>Lasers</subject><subject>Machines</subject><subject>Manufacturing</subject><subject>Mechatronics</subject><subject>Model accuracy</subject><subject>Monitoring</subject><subject>Neural networks</subject><subject>Nondestructive testing</subject><subject>Object recognition</subject><subject>Penalty 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monitoring laser based directed energy deposition process with deep convolutional neural network</title><author>Mi, Jiqian ; Zhang, Yikai ; Li, Hui ; Shen, Shengnan ; Yang, Yongqiang ; Song, Changhui ; Zhou, Xin ; Duan, Yucong ; Lu, Junwen ; Mai, Haibo</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c249t-ddc664fca19626c640dcb08ba260f4d2f9b1b0d986ba96e56a0c022a8bebff703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Advanced manufacturing technologies</topic><topic>Artificial neural networks</topic><topic>Business and Management</topic><topic>Control</topic><topic>Deposition</topic><topic>Image segmentation</topic><topic>Laser applications</topic><topic>Lasers</topic><topic>Machines</topic><topic>Manufacturing</topic><topic>Mechatronics</topic><topic>Model accuracy</topic><topic>Monitoring</topic><topic>Neural networks</topic><topic>Nondestructive 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Basic</collection><jtitle>Journal of intelligent manufacturing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mi, Jiqian</au><au>Zhang, Yikai</au><au>Li, Hui</au><au>Shen, Shengnan</au><au>Yang, Yongqiang</au><au>Song, Changhui</au><au>Zhou, Xin</au><au>Duan, Yucong</au><au>Lu, Junwen</au><au>Mai, Haibo</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>In-situ monitoring laser based directed energy deposition process with deep convolutional neural network</atitle><jtitle>Journal of intelligent manufacturing</jtitle><stitle>J Intell Manuf</stitle><date>2023-02-01</date><risdate>2023</risdate><volume>34</volume><issue>2</issue><spage>683</spage><epage>693</epage><pages>683-693</pages><issn>0956-5515</issn><eissn>1572-8145</eissn><abstract>Laser based directed energy deposition (L-DED) is a promising type of additive manufacturing technology. The non-destructive testing technology for the quality monitoring of L-DED processed parts is becoming more and more demanding in terms of accuracy, real-time, and ease of operation. This paper introduces a new image recognition system based on a deep convolutional neural network, which uses multiple lightweight architectures to reduce detection time. In order to eliminate the interference better, it improves the penalty function, which effectively improves the accuracy. Judging from the detection results of the data set, the accuracy of the model training reaches 94.71%, which achieves a very good image segmentation effect and solves the technical problem of in-situ monitoring of the L-DED process. This system realizes the positioning of the spatters for the first time, and at the same time, the number of spatters and area of molten pool are correlated to the laser scanning speed and the laser power.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s10845-021-01820-0</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-4404-8845</orcidid></addata></record> |
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| subjects | Accuracy Advanced manufacturing technologies Artificial neural networks Business and Management Control Deposition Image segmentation Laser applications Lasers Machines Manufacturing Mechatronics Model accuracy Monitoring Neural networks Nondestructive testing Object recognition Penalty function Processes Production Robotics |
| title | In-situ monitoring laser based directed energy deposition process with deep convolutional neural network |
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