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

被引:17
|
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
来源
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2023年 / 126卷 / 11-12期
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
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