A vision-based system for post-welding quality measurement and defect detection
The measurement of the welding bead and the followed evaluation of the weld quality is an important part in the industrial welding. However, the main difficulty in using visual inspection to evaluate the weld quality is the time taken and labor wasted. In order to solve these problems, an automated...
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| Veröffentlicht in: | International journal of advanced manufacturing technology Jg. 86; H. 9-12; S. 3007 - 3014 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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01.10.2016
Springer Nature B.V |
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| ISSN: | 0268-3768, 1433-3015 |
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| Abstract | The measurement of the welding bead and the followed evaluation of the weld quality is an important part in the industrial welding. However, the main difficulty in using visual inspection to evaluate the weld quality is the time taken and labor wasted. In order to solve these problems, an automated vision-based system is presented for non-destructive and online post-welding quality inspection. The trouble with respect to vision-based system is the robustness of image processing algorithms. This paper presents a novel image processing method that can automatically extract the weld joint profile and feature points, measure the weld bead size, and detect defects. At the same time, the three-dimensional (3D) profile of the weld surface can be reconstructed with the aim of monitoring the weld quality online. The proposed algorithm is validated through experiment in industrial environment. |
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| AbstractList | The measurement of the welding bead and the followed evaluation of the weld quality is an important part in the industrial welding. However, the main difficulty in using visual inspection to evaluate the weld quality is the time taken and labor wasted. In order to solve these problems, an automated vision-based system is presented for non-destructive and online post-welding quality inspection. The trouble with respect to vision-based system is the robustness of image processing algorithms. This paper presents a novel image processing method that can automatically extract the weld joint profile and feature points, measure the weld bead size, and detect defects. At the same time, the three-dimensional (3D) profile of the weld surface can be reconstructed with the aim of monitoring the weld quality online. The proposed algorithm is validated through experiment in industrial environment. |
| Author | Wang, Zong-Yi Chu, Hui-Hui |
| Author_xml | – sequence: 1 givenname: Hui-Hui surname: Chu fullname: Chu, Hui-Hui email: chuhuihui@hrbeu.edu.cn organization: College of Automation Harbin Engineering University – sequence: 2 givenname: Zong-Yi surname: Wang fullname: Wang, Zong-Yi organization: College of Automation Harbin Engineering University |
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| Cites_doi | 10.1007/s00170-011-3770-z 10.1117/12.301529 10.1007/s12541-014-0352-7 10.1109/TMECH.2003.816829 10.1109/TIM.2009.2028222 10.1049/ip-smt:19941145 10.1109/TPAMI.2007.70787 10.1109/AUTEST.1995.522699 10.3390/s110100506 |
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| Keywords | Weld quality inspection 3D reconstruction Machine vision Image processing algorithm |
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| References | NguyenHCLeeBRLaser-vision-based quality inspection system for small-bead laser weldingInt J Precis Eng Manuf201415341542310.1007/s12541-014-0352-7 HuangWKovacevicRA laser-based vision system for weld quality inspectionSensors201111150652110.3390/s110100506 Dar IM, Newman KE, Vachtsevanos G (1995) On-line inspection of surface mount devices using vision and infrared sensors. In Proc. Auto-test Conference 376–384 GonzalezRCWoodsREDigital image processing20083New JerseyPearson Education, Inc. Cook GE, Barnett RJ, Andersen K, Springfield JF, Strauss AM (1999) Automated visual inspection and interpretation system for weld quality evaluation. Conference Record, IEEE IAS Annual Meeting 2:1809–1816 ReichertCPre- and post-weld inspection using laser visionSPIE Proc1998339624425410.1117/12.301529 KumarGSNatarajanUAnanthanSSVision inspection system for the identification and classification of defects in MIG welding jointsInt J Adv Manuf Technol20126192393310.1007/s00170-011-3770-z HaiXLuoMWangCShenghuaYA line structured light 3D visual sensor calibration by vanishing point methodOpto-Electron Eng19962335358 ValleMDGallinaPGasparettoAMirror synthesis in a mechatronic system for superficial defect detectionIEEE/ASME Trans Mechatron20038330931710.1109/TMECH.2003.816829 ChumOMatasJOptimal randomized RANSACIEEE Trans Pattern Anal Mach Intell2008301472148210.1109/TPAMI.2007.70787 HuKZhouFQZhangGJFast extraction method for sub-pixel center of structured light stripeChin J Sci Instrum2006271013261329 WongBKElliottMPRapleyCWAutomatic casting surface defect recognition and classificationSPIE Proc19952423101105 WhiteRASmithJSLucasJVision-based gauge for online weld profile metrologyIEEE Proc Sci Meas Technol1994141652152610.1049/ip-smt:19941145 HeJJZhangGJStudy on method for processing image of stripe in structured light 3D vision measuring techniqueJ Beijing Univ Aeronaut Astronaut2003297593597 LiYLiYFWangQLXuDTanMMeasurement and defect detection of the weld bead based on online vision inspectionIEEE Trans Instrum Meas20105971841184910.1109/TIM.2009.2028222 GS Kumar (8334_CR9) 2012; 61 O Chum (8334_CR14) 2008; 30 Y Li (8334_CR3) 2010; 59 W Huang (8334_CR4) 2011; 11 C Reichert (8334_CR2) 1998; 3396 8334_CR5 BK Wong (8334_CR7) 1995; 2423 8334_CR1 X Hai (8334_CR15) 1996; 23 HC Nguyen (8334_CR8) 2014; 15 RC Gonzalez (8334_CR11) 2008 MD Valle (8334_CR6) 2003; 8 JJ He (8334_CR12) 2003; 29 K Hu (8334_CR13) 2006; 27 RA White (8334_CR10) 1994; 141 |
| References_xml | – reference: ReichertCPre- and post-weld inspection using laser visionSPIE Proc1998339624425410.1117/12.301529 – reference: KumarGSNatarajanUAnanthanSSVision inspection system for the identification and classification of defects in MIG welding jointsInt J Adv Manuf Technol20126192393310.1007/s00170-011-3770-z – reference: HuKZhouFQZhangGJFast extraction method for sub-pixel center of structured light stripeChin J Sci Instrum2006271013261329 – reference: LiYLiYFWangQLXuDTanMMeasurement and defect detection of the weld bead based on online vision inspectionIEEE Trans Instrum Meas20105971841184910.1109/TIM.2009.2028222 – reference: ChumOMatasJOptimal randomized RANSACIEEE Trans Pattern Anal Mach Intell2008301472148210.1109/TPAMI.2007.70787 – reference: Cook GE, Barnett RJ, Andersen K, Springfield JF, Strauss AM (1999) Automated visual inspection and interpretation system for weld quality evaluation. Conference Record, IEEE IAS Annual Meeting 2:1809–1816 – reference: HuangWKovacevicRA laser-based vision system for weld quality inspectionSensors201111150652110.3390/s110100506 – reference: Dar IM, Newman KE, Vachtsevanos G (1995) On-line inspection of surface mount devices using vision and infrared sensors. In Proc. Auto-test Conference 376–384 – reference: HaiXLuoMWangCShenghuaYA line structured light 3D visual sensor calibration by vanishing point methodOpto-Electron Eng19962335358 – reference: GonzalezRCWoodsREDigital image processing20083New JerseyPearson Education, Inc. – reference: HeJJZhangGJStudy on method for processing image of stripe in structured light 3D vision measuring techniqueJ Beijing Univ Aeronaut Astronaut2003297593597 – reference: WongBKElliottMPRapleyCWAutomatic casting surface defect recognition and classificationSPIE Proc19952423101105 – reference: ValleMDGallinaPGasparettoAMirror synthesis in a mechatronic system for superficial defect detectionIEEE/ASME Trans Mechatron20038330931710.1109/TMECH.2003.816829 – reference: NguyenHCLeeBRLaser-vision-based quality inspection system for small-bead laser weldingInt J Precis Eng Manuf201415341542310.1007/s12541-014-0352-7 – reference: WhiteRASmithJSLucasJVision-based gauge for online weld profile metrologyIEEE Proc Sci Meas Technol1994141652152610.1049/ip-smt:19941145 – volume: 61 start-page: 923 year: 2012 ident: 8334_CR9 publication-title: Int J Adv Manuf Technol doi: 10.1007/s00170-011-3770-z – volume: 23 start-page: 53 issue: 3 year: 1996 ident: 8334_CR15 publication-title: Opto-Electron Eng – volume: 3396 start-page: 244 year: 1998 ident: 8334_CR2 publication-title: SPIE Proc doi: 10.1117/12.301529 – volume: 2423 start-page: 101 year: 1995 ident: 8334_CR7 publication-title: SPIE Proc – volume: 29 start-page: 593 issue: 7 year: 2003 ident: 8334_CR12 publication-title: J Beijing Univ Aeronaut Astronaut – volume: 15 start-page: 415 issue: 3 year: 2014 ident: 8334_CR8 publication-title: Int J Precis Eng Manuf doi: 10.1007/s12541-014-0352-7 – volume: 8 start-page: 309 issue: 3 year: 2003 ident: 8334_CR6 publication-title: IEEE/ASME Trans Mechatron doi: 10.1109/TMECH.2003.816829 – volume: 59 start-page: 1841 issue: 7 year: 2010 ident: 8334_CR3 publication-title: IEEE Trans Instrum Meas doi: 10.1109/TIM.2009.2028222 – volume: 141 start-page: 521 issue: 6 year: 1994 ident: 8334_CR10 publication-title: IEEE Proc Sci Meas Technol doi: 10.1049/ip-smt:19941145 – volume: 30 start-page: 1472 year: 2008 ident: 8334_CR14 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2007.70787 – volume: 27 start-page: 1326 issue: 10 year: 2006 ident: 8334_CR13 publication-title: Chin J Sci Instrum – ident: 8334_CR5 doi: 10.1109/AUTEST.1995.522699 – volume: 11 start-page: 506 issue: 1 year: 2011 ident: 8334_CR4 publication-title: Sensors doi: 10.3390/s110100506 – volume-title: Digital image processing year: 2008 ident: 8334_CR11 – ident: 8334_CR1 |
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| SubjectTerms | Algorithms Automatic welding CAE) and Design Computer-Aided Engineering (CAD Engineering Evaluation Feature extraction Image processing Industrial and Production Engineering Inspection Mechanical Engineering Media Management Original Article Production planning Quality Quality assessment Repair & maintenance Vision systems Weld bead size Welded joints Welding |
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| Title | A vision-based system for post-welding quality measurement and defect detection |
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