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
[111]   Monitoring of weld joint penetration during variable polarity plasma arc welding based on the keyhole characteristics and PSO-ANFIS [J].
Wu, Di ;
Chen, Huabin ;
Huang, Yiming ;
He, Yinshui ;
Hu, Minghua ;
Chen, Shanben .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2017, 239 :113-124
[112]   A study on Development of Optimal Noise Filter Algorithm for Laser Vision System in GMA Welding [J].
Wu, Qian-Qian ;
Lee, Jong-Pyo ;
Park, Min-Ho ;
Park, Cheol-Kyun ;
Kim, Ill-Soo .
12TH GLOBAL CONGRESS ON MANUFACTURING AND MANAGEMENT (GCMM - 2014), 2014, 97 :819-827
[113]   A detection algorithm of spatter on welding plate surface based on machine vision [J].
Xia Xin-miao ;
Jiang Zhao-liang ;
Xu Peng-fei .
OPTOELECTRONICS LETTERS, 2019, 15 (01) :52-56
[114]   Towards monitoring laser welding process via a coaxial pyrometer [J].
Xiao, Xianfeng ;
Liu, Xingbo ;
Cheng, Manping ;
Song, Lijun .
JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2020, 277 (277)
[115]   Active vision sensing and feedback control of back penetration for thin sheet aluminum alloy in pulsed MIG suspension welding [J].
Xiong, Jun ;
Zou, Shuangyang .
JOURNAL OF PROCESS CONTROL, 2019, 77 :89-96
[116]   Real-time image processing for vision-based weld seam tracking in robotic GMAW [J].
Xu, Yanling ;
Fang, Gu ;
Chen, Shanben ;
Zou, Ju Jia ;
Ye, Zhen .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2014, 73 (9-12) :1413-1425
[117]  
Yang H, 2008, WOODHEAD PUBL MATER, P74, DOI 10.1533/9781845694401.1.74
[118]   An Automatic Detection and Identification Method of Welded Joints Based on Deep Neural Network [J].
Yang, Lei ;
Liu, Yanhong ;
Peng, Jinzhu .
IEEE ACCESS, 2019, 7 :164952-164961
[119]   Temperature monitoring and calibration in Ti-6Al-4V molten pool with pulsed arc welding [J].
Yang, Mingxuan ;
Bai, Ruirui ;
Zheng, Hao ;
Qi, Bojin .
SCIENCE AND TECHNOLOGY OF WELDING AND JOINING, 2020, 25 (05) :369-376
[120]   Quantitative evaluation method of arc sound spectrum based on sample entropy [J].
Yao, Ping ;
Zhou, Kang ;
Zhu, Qiang .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2017, 92 :379-390