Recent developments in computer vision and artificial intelligence aided intelligent robotic welding applications

被引:23
作者
Eren, Berkay [1 ,2 ]
Demir, Mehmet Hakan [1 ,3 ]
Mistikoglu, Selcuk [2 ]
机构
[1] Iskenderun Tech Univ, Dept Mechatron Engn, TR-31200 Hatay, Turkiye
[2] Iskenderun Tech Univ, Dept Mech Engn, TR-31200 Hatay, Turkiye
[3] Univ Illinois, Dept Mech & Ind Engn, Chicago, IL 60607 USA
关键词
Robotic welding; Vision sensing; Image processing; Seam tracking; Machine learning; Deep learning; SEAM-TRACKING SYSTEM; HIGH-PRECISION MEASUREMENT; CONVOLUTION OPERATOR; FEATURE-EXTRACTION; CALIBRATION METHOD; INDUSTRIAL ROBOT; DEFECT DETECTION; PARTICLE FILTER; GTAW PROCESS; FILLET WELD;
D O I
10.1007/s00170-023-11456-4
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The welding process, which is an indispensable part of the manufacturing industry, has been in demand for years and continues to attract the attention of researchers. With the transition to Industry 4.0, the welding process got out of the control of the operators and became automated with sensors and artificial intelligence methods, and as a result, it became inevitable for industrial manipulators or robots to enter the production sector. One of the most important details in making the welding process autonomous in manufacturing is the sensors, and among the sensors are the vision sensors. In recent years, it is seen that robotic welding applications are applied very sensitively and successfully when visual sense and artificial intelligence are used together. This study comprehensively reviewed research and development for cutting-edge applications using visual sensors and artificial intelligence for robotic welding applications. The processes that are the subject of intelligent robotic welding applications such as calibration, determination of welding starting point, seam tracking, and welding quality are determined and discussed based on current studies and critical analyzes. The detection, tracking, diagnosis, classification, and prediction performances of various methods of machine learning (ML), which is one of the most used areas in artificial intelligence-based applications, in welding applications are examined comparatively. This review article will help researchers about what should be considered in vision sensor aided robotic welding applications and how to contribute more to studies with artificial intelligence support.
引用
收藏
页码:4763 / 4809
页数:47
相关论文
共 178 条
[101]  
Shichao Deng, 2021, Journal of Physics: Conference Series, V1820, DOI 10.1088/1742-6596/1820/1/012007
[102]   Review of Deep Learning Algorithms and Architectures [J].
Shrestha, Ajay ;
Mahmood, Ausif .
IEEE ACCESS, 2019, 7 :53040-53065
[103]  
Singh A, 2016, PROCEEDINGS OF THE 10TH INDIACOM - 2016 3RD INTERNATIONAL CONFERENCE ON COMPUTING FOR SUSTAINABLE GLOBAL DEVELOPMENT, P1310
[104]   Monitoring of friction stir welding based on vision system coupled with Machine learning algorithm [J].
Sudhagar, S. ;
Sakthivel, M. ;
Ganeshkumar, P. .
MEASUREMENT, 2019, 144 :135-143
[105]   Robotic Tactile Sensor System and Applications [J].
Suwanratchatamanee, Kitti ;
Matsumoto, Mitsuharu ;
Hashimoto, Shuji .
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2010, 57 (03) :1074-1087
[106]   Advanced monitoring of machining operations [J].
Teti, R. ;
Jemielniak, K. ;
O'Donnell, G. ;
Dornfeld, D. .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2010, 59 (02) :717-739
[107]   Investigation of feed-forward back propagation ANN using voltage signals for the early prediction of the welding defect [J].
Thekkuden, Dinu Thomas ;
Mourad, Abdel-Hamid Ismail .
SN APPLIED SCIENCES, 2019, 1 (12)
[108]   Robust identification of weld seam based on region of interest operation [J].
Tian, Ying-Zhong ;
Liu, Hong-Fei ;
Li, Long ;
Wang, Wen-Bin ;
Feng, Jie-Cai ;
Xi, Feng-Feng ;
Yuan, Guang-Jie .
ADVANCES IN MANUFACTURING, 2020, 8 (04) :473-485
[109]   Welding defect detection: coping with artifacts in the production line [J].
Tripicchio, Paolo ;
Camacho-Gonzalez, Gerardo ;
D'Avella, Salvatore .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2020, 111 (5-6) :1659-1669
[110]   Intelligent welding system technologies: State-of-the-art review and perspectives [J].
Wang, Baicun ;
Hu, S. Jack ;
Sun, Lei ;
Freiheit, Theodor .
JOURNAL OF MANUFACTURING SYSTEMS, 2020, 56 :373-391