Advanced Search
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
Citation: 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

MIG weld seam tracking system based on image automatic enhancement and attention mechanism deep learning

More Information
  • Received Date: July 17, 2024
  • Available Online: September 23, 2024
  • Aiming at the problem that conventional MIG welding is difficult to adjust the welding position in real time according to the group deviation and thermal accumulation deformation, a weld seam tracking method based on passive vision is proposed. Through the image spatial domain filtering and automatic enhancement algorithm, the YOLO v7 deep learning model with attention mechanism is used to extract and analyze the groove alignment position and arc position in the region of interest in real time. The fuzzy control algorithm is used to control the MIG welding process in real time when the preset deviation occurs. The results show that, the image automatic enhancement algorithm is used to complete the preprocessing of the image, and the pixel gray value of the edge position is increased from 40 to about 110, which significantly improves the accuracy of the edge position information extraction; Based on the YOLO v7 network structure, the attention mechanism module is added to improve the efficiency of target detection, and the mAP index is as high as 99.27%. The preset deviation test shows that the pixel error of the alignment deviation detection is within 8 pixels, and the alignment deviation distance is controlled between ± 0.5 mm.

  • [1]
    韩庆璘, 李大用, 李鑫磊, 等. 基于分区减光的电弧增材制造熔敷道尺寸主被动联合视觉检测[J]. 焊接学报, 2020, 41(9): 28 − 32. doi: 10.12073/j.hjxb.20200418001

    Han Qinglin, Li Dayong, Li Xinlei, et al. Bead geometry measurement for wire and arc additive manufacturing using active-passive composite vision sensing based on regional filter[J]. Transactions of the China Welding Institution, 2020, 41(9): 28 − 32. doi: 10.12073/j.hjxb.20200418001
    [2]
    陈华斌, 陈善本. 复杂场景下的焊接智能制造中的信息感知与控制方法[J]. 金属学报, 2022, 58(4): 541 − 550. doi: 10.11900/0412.1961.2021.00528

    Chen Huabin, Chen Shanben. Key information perception and control strategy of intellignet welding under complex scene[J]. Acta Metallurgica Sinica, 2022, 58(4): 541 − 550. doi: 10.11900/0412.1961.2021.00528
    [3]
    Xia Lei, Zhou Jianping, Xue Ruilei, et al. Real-time seam tracking during narrow gap GMAW process based on the wide dynamic vision sensing method[J]. Journal of Manufacturing Processes, 2023, 101: 820 − 834. doi: 10.1016/j.jmapro.2023.06.045
    [4]
    Xiao Runquan, Xu Yanling, Hou Zhen, et al. A feature extraction algorithm based on improved Snake model for multi-pass seam tracking in robotic arc welding[J]. Journal of Manufacturing Processes, 2021, 72: 48 − 60. doi: 10.1016/j.jmapro.2021.10.005
    [5]
    张广军, 冷孝宇, 吴林. 弧焊机器人结构光视觉传感焊缝跟踪[J]. 焊接学报, 2008, 29(9): 8 − 10. doi: 10.3321/j.issn:0253-360X.2008.09.003

    Zhang Guangjun, Len Xiaoyu, Wu Lin. Robotic welding seam tracing system based on structure light vision sensor[J]. Transactions of the China Welding Institution, 2008, 29(9): 8 − 10. doi: 10.3321/j.issn:0253-360X.2008.09.003
    [6]
    Wang Weixi, Yamane Satoshi, Wang Qi, et al. Visual sensing and quality control in plasma MIG welding[J]. Journal of Manufacturing Processes, 2023, 86: 163 − 176. doi: 10.1016/j.jmapro.2022.12.041
    [7]
    Zhang Zhifen, Wen Guangrui, Chen Shanben. Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding[J]. Journal of Manufacturing Processes, 2019, 45: 208 − 216. doi: 10.1016/j.jmapro.2019.06.023
  • Related Articles

    [1]GAI Shengnan, WANG Yu, XU Bin, WANG Kai, XIAO Jun, CHEN Shujun. Monitoring the formation of aluminum alloy TIG weld seams based on passive vision[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION. DOI: 10.12073/j.hjxb.20240102001
    [2]HONG Yuxiang, YING Qiluo, LIN Kai, WANG Kaiming, WANG Yaoqi. Image recognition of arc molten pool based on attention mechanism and transfer learning[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION. DOI: 10.12073/j.hjxb.20240112003
    [3]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
    [4]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
    [5]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
    [6]XIAO Yang, GAO Weixin, DENG Guohao. Recognition algorithm of small-diameter tube X-ray welding defect image[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(2): 82-88. DOI: 10.12073/j.hjxb.20230228001
    [7]GUO Zhongfeng, LIU Junchi, YANG Junlin. Weld recognition based on key point detection method[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(1): 88-93. DOI: 10.12073/j.hjxb.20230204001
    [8]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
    [9]LIU Tianyuan, BAO Jinsong, WANG Junliang, ZHENG Xiaohu, WANG Jiacheng. Adaptive edge detection of molten pool based on coarse-grained regularization in restricted solution space[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2020, 41(12): 49-54. DOI: 10.12073/j.hjxb.20200815002
    [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

Catalog

    Article views (76) PDF downloads (27) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return