Advanced Search
XIAO Wenbo, HE Yinshui, YUAN Haitao, MA Guohong. Synchronous real-time detection of weld bead geometry and the welding torch in galvanized steel GAMW[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(12): 78-82. DOI: 10.12073/j.hjxb.20201021001
Citation: XIAO Wenbo, HE Yinshui, YUAN Haitao, MA Guohong. Synchronous real-time detection of weld bead geometry and the welding torch in galvanized steel GAMW[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2021, 42(12): 78-82. DOI: 10.12073/j.hjxb.20201021001

Synchronous real-time detection of weld bead geometry and the welding torch in galvanized steel GAMW

More Information
  • Received Date: October 20, 2020
  • Available Online: December 22, 2021
  • This paper presented a method to synchronously detect all-position weld bead geometry and the welding torch in lap joint GMAW with galvanized sheets, in which laser vision sensing was used. In this method a laser was used to simultaneously detect the weld bead and the welding torch, and the scale-invariant feature transform algorithm and orientation feature detection algorithm were used to extract the weld profile as well as the welding torch. The feature points of the profile were identified with the Harris corner detection algorithm. Since the weld area is relatively small, a sub pixel level measurement method was proposed to obtain the all-position bead height, width and area. The Weld center of gravity was detected with the zero and first moment. Experimental results showed the strong adaptability and high accuracy of that this proposed method. It provides the possibility of adjusting the attitude of the welding torch and welding process parameters to control weld formation online.
  • Jahanzaib M, Hussain S, Wasim A, et al. Modeling of weld bead geometry on HSLA steel using response surface methodology[J]. The International Journal of Advanced Manufacturing Technology, 2017, 89(5): 2087 − 2098.
    Neelamegam C, Sapineni V, Muthukumaran V, et al. Hybrid intelligent modeling for optimizing welding process parameters for reduced activation ferritic-martensitic (RAFM) steel[J]. Journal of Intelligent Learning Systems and Applications, 2013, 5(1): 39 − 47.
    Om H, Pandey S. Effect of heat input on dilution and heat affected zone in submerged arc welding process[J]. Sadhana, 2013, 38(6): 1369 − 1391. doi: 10.1007/s12046-013-0182-9
    Chen C, Fan C, Cai X, et al. Investigation of formation and microstructure of Ti-6Al-4V weld bead during pulse ultrasound assisted TIG welding[J]. Journal of Manufacturing Processes, 2019, 46: 241 − 247. doi: 10.1016/j.jmapro.2019.09.014
    Xiong J, Zhang G. Online measurement of bead geometry in GMAW-based additive manufacturing using passive vision[J]. Measurement Science and Technology, 2013, 24(11): 115103. doi: 10.1088/0957-0233/24/11/115103
    He Y, Li D, Pan Z, et al. Dynamic modeling of weld bead geometry features in thick plate GMAW based on machine vision and learning[J]. Sensors, 2020, 20(24): 7104 − 7122. doi: 10.3390/s20247104
    Yang L, Fan J, Liu Y, et al. Automatic detection and location of weld beads with deep convolutional neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 1 − 12.
    Xiao Jun, Wen Tao, Chen Shujun, et al. Teleoperation strategies research for collaborative welding systems based on virtual reality[J]. China Welding, 2020, 29(2): 38 − 47.
    Kalwasiński D. The position for experimental research to simulate sense of touch[J]. Mechanik, 2016, 89(7): 718 − 720.
    潘海鸿, 尹华壬, 梁旭斌, 等. 可调整焊枪姿态直线摆弧路径算法研究[J]. 组合机床与自动化加工技术, 2019, 11: 37 − 41.

    Pan Haihong, Yin Huaren, Liang Xubin, et al. Research on the algorithm of linear swing arc path with adjustable welding torch attitude[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2019, 11: 37 − 41.
    余卓骅, 胡艳梅, 何银水. 薄板机器人自动焊接焊枪三维偏差的有效提取[J]. 焊接学报, 2019, 40(11): 49 − 53. doi: 10.12073/j.hjxb.2019400287

    Yu Zhuohua, Hu Yanmei, He Yinshui. Effective three-dimensional deviation extraction of the welding torch for robotic arc welding with steel sheets[J]. Transactions of the China Welding Institution, 2019, 40(11): 49 − 53. doi: 10.12073/j.hjxb.2019400287
    Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91 − 110. doi: 10.1023/B:VISI.0000029664.99615.94
  • Related Articles

    [1]HUANG Hongxing, WU Di, ZENG Da, PENG Biao, SUN Tao, ZHANG Peilei, SHI Haichuan. Quantitative evaluation of spatter in adjustable ring mode laser welding based on In-situ OCT measurement[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 128-132. DOI: 10.12073/j.hjxb.20240715002
    [2]WANG Jie, ZHANG Zhifen, BAI Zijian, ZHANG Shuai, QIN Rui, WEN Guangrui, CHEN Xuefeng. Welding forming quality monitoring based on CNN-LSTM hybrid drive[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 121-127. DOI: 10.12073/j.hjxb.20240707002
    [3]BAI Zijian, ZHANG Zhifen, WANG Jie, ZHANG Shuai, SU Yu, WEN Guangrui, CHEN Xuefeng. Dilution rate monitoring of DED based on a spectral physical feature perception network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 95-100. DOI: 10.12073/j.hjxb.20240701002
    [4]ZHU Ming, LEI Runji, WENG Jun, WANG Jincheng, SHI Yu. MIG weld seam tracking system based on image automatic enhancement and attention mechanism deep learning[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 90-94. DOI: 10.12073/j.hjxb.20240718002
    [5]MA Jiawei, SUN Jingbo, CHI Guanxin, ZHANG Guangjun, LI Xinlei. Weld identification and robot path planning algorithm based on stereo vision and YOLO deep learning framework[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 45-49. DOI: 10.12073/j.hjxb.20240720001
    [6]WANG Rui, GAO Shaoze, LIU Weipeng, WANG Gang. A lightweight and efficient X-ray weld image defect detection method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(7): 41-49. DOI: 10.12073/j.hjxb.20230630003
    [7]BAI Zijian, LI Zhiwen, ZHANG Zhifen, QIN Rui, ZHANG Shuai, XU Yaowen, WEN Guangrui. On-line monitoring of TIG welding quality of nuclear power plug tube based on arc spectrum[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(5): 8-19. DOI: 10.12073/j.hjxb.20230610002
    [8]XU Donghui, MENG Fanpeng, SUN Peng, ZHENG Xuchen, CHENG Yongchao, MA Zhi, CHEN Shujun. Online monitoring of GMAW welding defect based on deep learning[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(3): 114-119. DOI: 10.12073/j.hjxb.20230117002
    [9]WANG Rui, HU Yunlei, LIU Weipeng, LI Haitao. Defect detection of weld X-ray image based on edge AI[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(1): 79-84. DOI: 10.12073/j.hjxb.20210516001
    [10]LI Hexi, HAN Xinle, FANG Zaojun. A visual model of welding robot based on CNN deep learning[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(2): 154-160. DOI: 10.12073/j.hjxb.2019400060
  • Cited by

    Periodical cited type(5)

    1. 韩莹,何实,吕晓春,郭枭,焦帅杰. 粗晶铝合金超塑性变形机理的研究现状. 焊接. 2023(03): 11-21+26 .
    2. 谢吉林,汪洪伟,陈玉华,刘文阔,张体明,王善林. Al/Mg搅拌摩擦点焊–钎焊接头的微观组织与拉伸剪切性能研究. 航空制造技术. 2023(11): 59-65+76 .
    3. 刘文阔,谢吉林,陈玉华,汪洪伟,张体明,王善林. 搅拌针对FSSW-B接头界面组织与力学性能的影响. 稀有金属材料与工程. 2023(07): 2468-2477 .
    4. 舒伟. 基于铝合金先进焊接工艺的探索. 现代制造技术与装备. 2022(03): 171-173 .
    5. 余光伟,谢清程,杨珺柳,施睿赟,蔡翔宇,李文海. 油管模型建立及结构对称性对其残余应力影响. 焊接学报. 2022(03): 101-112+119-120 . 本站查看

    Other cited types(1)

Catalog

    Article views (261) PDF downloads (26) Cited by(6)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return