Research and prospect of welding monitoring technology based on machine vision

被引:52
作者
Fan, Xi'an [1 ]
Gao, Xiangdong [1 ]
Liu, Guiqian [1 ]
Ma, Nvjie [1 ]
Zhang, Yanxi [1 ]
机构
[1] Guangdong Univ Technol, Guangdong Prov Welding Engn Technol Res Ctr, Guangzhou 510006, Peoples R China
关键词
Welding; Process monitoring; Machine vision; Artificial intelligence; Optical sensor; Image processing; Optimization algorithm; MOLTEN POOL MORPHOLOGY; REAL-TIME MEASUREMENT; NEURAL-NETWORK; SEAM TRACKING; DROPLET TRANSFER; FEATURE-EXTRACTION; JOINT PENETRATION; SPATTER DETECTION; KEYHOLE GEOMETRY; THERMAL IMAGE;
D O I
10.1007/s00170-021-07398-4
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Welding monitoring technology based on machine vision has been widely researched in academic and industry, especially in the background of Industry 4.0, in that it can contribute to welding quality and productivity improvement. This paper outlines the technical points of welding status monitoring based on machine vision, including hardware and software. First of all, in the hardware part, the active and passive vision systems are briefly introduced, as well as the key steps in experimental deployment, such as the configuration of optical sensors and optical filters based on different detection objects. Secondly, some related image processing techniques in welding monitoring are also comprehensively reviewed. Additionally, the observed objects and their morphological characteristics of vision-based welding process monitoring are enumerated. On this basis, a series of intelligent models as well as optimization methods for recognition and classification in visual monitoring are considered in detail. Finally, potential research challenges and open research issues of welding visual monitoring are discussed to present an insight into future research opportunities. The main purpose of this paper is to provide a reference source for the researchers involved in intelligent robot welding.
引用
收藏
页码:3365 / 3391
页数:27
相关论文
共 146 条
[81]   Welding seam profiling techniques based on active vision sensing for intelligent robotic welding [J].
Muhammad, Jawad ;
Altun, Halis ;
Abo-Serie, Essam .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2017, 88 (1-4) :127-145
[82]   Monitoring of varying joint gap width during laser beam welding by a dual vision and spectroscopic sensing system [J].
Nilsen, Morgan ;
Sikstrom, Fredrik ;
Christiansson, Anna-Karin ;
Ancona, Antonio .
16TH NORDIC LASER MATERIALS PROCESSING CONFERENCE, NOLAMP16, 2017, 89 :100-107
[83]   Challenges to the interpretation of the electromagnetic feedback from laser welding [J].
Olsson, R. ;
Eriksson, I. ;
Powell, J. ;
Langtry, A. V. ;
Kaplan, A. F. H. .
OPTICS AND LASERS IN ENGINEERING, 2011, 49 (02) :188-194
[84]   Effect of shielding gas on laser-MAG arc hybrid welding results of thick high-tensile-strength steel plates [J].
Pan, Qinglong ;
Mizutani, Masami ;
Kawahito, Yousuke ;
Katayama, Seiji .
WELDING IN THE WORLD, 2016, 60 (04) :653-664
[85]   High power disk laser-metal active gas arc hybrid welding of thick high tensile strength steel plates [J].
Pan, Qinglong ;
Mizutani, Masami ;
Kawahito, Yousuke ;
Katayama, Seiji .
JOURNAL OF LASER APPLICATIONS, 2016, 28 (01)
[86]  
Pasinetti S, 2018, 2018 IEEE INTERNATIONAL WORKSHOP ON METROLOGY FOR INDUSTRY 4.0 AND IOT (METROIND4.0&IOT), P134, DOI 10.1109/METROI4.2018.8428332
[87]   Real-Time Measurement of Width and Height of Weld Beads in GMAW Processes [J].
Pinto-Lopera, Jesus Emilio ;
Motta, Jose Mauricio S. T. ;
Absi Alfaro, Sadek Crisostomo .
SENSORS, 2016, 16 (09)
[88]   Monitoring and adaptive control of laser processes [J].
Purtonen, Tuomas ;
Kalliosaari, Anne ;
Salminen, Antti .
8TH INTERNATIONAL CONFERENCE ON LASER ASSISTED NET SHAPE ENGINEERING (LANE 2014), 2014, 56 :1218-1231
[89]   Classification and identification of surface defects in friction stir welding: An image processing approach [J].
Ranjan, Ravi ;
Khan, Aaquib Reza ;
Parikh, Chirag ;
Jain, Rahul ;
Mahto, Raju Prasad ;
Pal, Srikanta ;
Pal, Surjya K. ;
Chakravarty, Debashish .
JOURNAL OF MANUFACTURING PROCESSES, 2016, 22 :237-253
[90]   Analysis of spatters in laser welding with beam oscillation: A machine vision approach [J].
Schweier, M. ;
Haubold, M. W. ;
Zaeh, M. F. .
CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY, 2016, 14 :35-42