Advanced Search
ZHANG Zhifen, CHEN Shanben, ZHANG Yuming, WEN Guangrui. Research progress and prospect of welding intelligent monitoring technology[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 10-20, 70. DOI: 10.12073/j.hjxb.20240707001
Citation: ZHANG Zhifen, CHEN Shanben, ZHANG Yuming, WEN Guangrui. Research progress and prospect of welding intelligent monitoring technology[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 10-20, 70. DOI: 10.12073/j.hjxb.20240707001

Research progress and prospect of welding intelligent monitoring technology

More Information
  • Received Date: July 06, 2024
  • Available Online: November 03, 2024
  • In the context of the National 14th Five-Year Plan for Intelligent Manufacturing and the Vision 2035 for High-Quality Development of Manufacturing, the significance of intelligent welding technology is readily apparent. First, this paper analyzes the publication status of research results in both academia and industry, summarizing the characteristics of the current distribution of achievements. Additionally, a series of important academic conferences related to this field are listed, highlighting the research popularity of this discipline. Furthermore, this review examines the latest domestic and international research progress in welding and additive manufacturing technologies from the perspectives of acoustics, spectroscopy, vision, thermography, and multi-information fusion monitoring. It indicates that multi-source information fusion technology is likely to be the mainstream approach for the future development of intelligent welding monitoring systems. Finally, the paper concluded the ‘six more and six less’ phenomena existing in the current stage of the domestic welding intelligence - defects online monitoring basic research, and from the multi-scenario to expand the application, pointed out the welding intelligent monitoring technology of the future development goals and key breakthroughs in the problem.

  • [1]
    房海基, 吕波, 张艳喜, 等. 焊接过程声信号在线检测技术现状与展望[J]. 紧密成形工程, 2022, 14(1): 165 − 172.

    Fang Haiji, Lü Bo, Zhang Yanxi, et al. Status and prospect of on-line acoustic signal detection technology in welding[J]. Journal of Netshape Forming Engineering, 2022, 14(1): 165 − 172.
    [2]
    Li Kaiqiang, Li Tao, Ma Min, et al. Laser cladding state recognition and crack defect diagnosis by acoustic emission signal and neural network[J]. Optics & Laser Technology, 2021, 142: 107161.
    [3]
    Hauser T, Reisch R T, Kamps T, et al. Acoustic emissions in directed energy deposition processes[J]. The International Journal of Advanced Manufacturing Technology, 2022, 119(5-6): 3517 − 3532. doi: 10.1007/s00170-021-08598-8
    [4]
    Li Hao, Gao Fei, Jiao Jinyang, et al. Acoustic emission-based cross-domain process health monitoring for additive manufacturing[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3320740.
    [5]
    Luo Zhongyi, Wu Di, Zhang Peilei, et al. Laser welding penetration monitoring based on time-frequency characterization of acoustic emission and CNN-LSTM hybrid network[J]. Materials, 2023, 16(4): 1614. doi: 10.3390/ma16041614
    [6]
    Ren Wenjing, Wen Guangrui, Xu Bin, et al. A novel convolutional neural network based on time–frequency spectrogram of arc sound and its application on GTAW penetration classification[J]. IEEE Transactions on Industrial Informatics, 2020, 17(2): 809 − 819.
    [7]
    Huang Jing, Zhang Zhifen, Qin Rui, et al. Interpretable real-time monitoring of pipeline weld crack leakage based on wavelet multi-kernel network[J]. Journal of Manufacturing Systems, 2024, 72: 93 − 103. doi: 10.1016/j.jmsy.2023.11.004
    [8]
    Qin Rui, Huang Jing, Zhang Zhifen, et al. An adaptive cepstrum feature representation method with variable frame length and variable filter banks for acoustic emission signals[J]. Mechanical Systems and Signal Processing, 2024, 208: 111031. doi: 10.1016/j.ymssp.2023.111031
    [9]
    Zhao Cang, Fezzaa K, Cunningham R W, et al. Real-time monitoring of laser powder bed fusion process using high-speed X-ray imaging and diffraction[J]. Scientific Reports, 2017, 7(1): 3602. doi: 10.1038/s41598-017-03761-2
    [10]
    Li Zhiwen, Zhang Zhifen, Zhang Shuai, et al. A novel approach of online monitoring for laser powder bed fusion defects: Air-borne acoustic emission and deep transfer learning[J]. Journal of Manufacturing Processes, 2023, 102: 579 − 592. doi: 10.1016/j.jmapro.2023.07.064
    [11]
    Zhang Shuai, Zhang Zhifen, Chen Xizhang, et al. Intra-layer and inter-layer monitoring of laser powder bed fusion defects based on airborne acoustic and gn-Res model: pore and deformation[J]. Virtual and Physical Prototyping, 2024, 19(1): e2360699. doi: 10.1080/17452759.2024.2360699
    [12]
    Ramalho A, Santos T G, Bevans B, et al. Effect of contaminations on the acoustic emissions during wire and arc additive manufacturing of 316L stainless steel[J]. Additive Manufacturing, 2022, 51: 102585. doi: 10.1016/j.addma.2021.102585
    [13]
    陈华斌, 孔萌, 吕娜, 等. 视觉传感技术在机器人智能化焊接中的研究现状[J]. 电焊机, 2017, 47(3): 1-16.

    Chen Huabin, Kong Meng, Lü Na, et al. Status and development of vision sensors on intelligentized robotic welding techologies. Electric Welding Machine, 2017, 47(3): 1-16.
    [14]
    Wang Qiyue, Jiao Wenhua, Zhang Yuming. Deep learning-empowered digital twin for visualized weld joint growth monitoring and penetration control[J]. Journal of Manufacturing Systems, 2020, 57: 429 − 439. doi: 10.1016/j.jmsy.2020.10.002
    [15]
    王振民, 张丽玲, 薛家祥. 基于视觉处理的焊缝质量检测与控制系统[J]. 焊接技术, 2007(3): 60 − 62 + 4. doi: 10.3969/j.issn.1002-025X.2007.03.023

    Wang Zhenmin, Xhang Liling, Xue Jiaxiang. A novel welding quality detecting and controlling system based on machine vision technique[J]. Welding Technology, 2007(3): 60 − 62 + 4. doi: 10.3969/j.issn.1002-025X.2007.03.023
    [16]
    Xia Chunyang, Pan Zengxi, Zhang Shiyu, et al. Model predictive control of layer width in wire arc additive manufacturing[J]. Journal of Manufacturing Processes, 2020, 58: 179 − 186. doi: 10.1016/j.jmapro.2020.07.060
    [17]
    He Yinshui, Ma Guohong, Chen Shanben, et al. Autonomous decision-making of welding position during multipass GMAW with T-joints: A Bayesian network approach[J]. IEEE Transactions on Industrial Electronics, 2021, 69(4): 3909 − 3917 .
    [18]
    Huang Junfen, Xue Long, Huang Jiqiang, et al. GMAW penetration state prediction based on visual sensing[J]. Journal of Mechanical Engineering, 2019, 55(17): 41 − 47. doi: 10.3901/JME.2019.17.041
    [19]
    Liu Tianyuan, Bao Jinsong, Wang Junliang, et al. Laser welding penetration state recognition method fused with timing information[J]. Chinese Journal of Lasers, 2021, 48(6): 0602119. doi: 10.3788/CJL202148.0602119
    [20]
    洪宇翔, 杨明轩, 都东, 等. 铝合金爬坡TIG焊熔池失稳状态的视觉检测[J]. 焊接学报, 2021, 42(10): 8 − 13 + 97-98.

    Hong Xiangyu, Yang Mingxuan, Du Dong, et al. Unstable state vision detection of molten pool during aluminum alloy climbing-TIG welding[J]. Transactions of the China Welding Institution, 2021, 42(10): 8 − 13 + 97-98.
    [21]
    Xiao Runquan, Xu Yanling, Hou Zhen, et al. A novel visual guidance framework for robotic welding based on binocular cooperation[J]. Robotics and Computer-Integrated Manufacturing, 2022, 78: 102393. doi: 10.1016/j.rcim.2022.102393
    [22]
    Wang Jie, Zhang Zhifen, Qin Rui, et al. Online identification of burn-through and weld deviation in sheet lap MIG welding based on YOLOv5[J]. Measurement Science and Technology, 2023, 35(2): 025119.
    [23]
    Zhang Chen, Gao Ming, Chen Cong, et al. Spectral diagnosis of wire arc additive manufacturing of Al alloys[J]. Additive Manufacturing, 2019, 30: 100869. doi: 10.1016/j.addma.2019.100869
    [24]
    白子键, 李治文, 张志芬, 等. 基于电弧光谱的核电堵管TIG焊接质量在线监测[J]. 焊接学报, 2024, 45(5): 8 − 19. doi: 10.12073/j.hjxb.20230610002

    Bai Zijian, Li Zhiwen, Zhang Zhifen, et al. 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
    [25]
    李晨星, 肖笑, 孟令燃, 等. 电弧增材制造的热源光谱分析[J]. 材料热处理学报, 2022, 23(8): 186 − 194.

    Li Xincheng, Xiao Xiao, Meng Lingran, et al. Spectral analysis of heat source in arc additive manufacturing[J]. Transactions of Materials and Heat Treatment, 2022, 23(8): 186 − 194.
    [26]
    Huang Yiming, Zhang Feng, Yuan Jiong, et al. Investigation on surface morphology and microstructure of double-wire + arc additive manufactured aluminum alloys based on spectral analysis[J]. Journal of Manufacturing Processes, 2022, 84: 639 − 651. doi: 10.1016/j.jmapro.2022.10.043
    [27]
    Chen Lin, Yang Fei, Wang Rui, et al. Optical spectral physics-informed attention network for condition monitoring in WAAM[J]. IEEE Transactions on Industrial Electronics, 2024, 71(8): 9708 − 9718. doi: 10.1109/TIE.2023.3325570
    [28]
    Lednev V N, Sdvizhenskii P A. Asyutin R D, et al. In situ multi-elemental analysis by laser induced breakdown spectroscopy in additive manufacturing[J]. Additive Manufacturing, 2019, 25: 64 − 70. doi: 10.1016/j.addma.2018.10.043
    [29]
    Yang Dongqing, Wang Gang, Zhang Guangjun. Thermal analysis for single-pass multi-layer GMAW based additive manufacturing using infrared thermography[J]. Journal of Materials Processing Technology, 2017, 244: 215 − 224. doi: 10.1016/j.jmatprotec.2017.01.024
    [30]
    曹宏岩, 陈希章, 胡超, 等. 基于红外温度场的焊接质量在线检测方法[J]. 上海交通大学学报, 2016, 50(S1): 66 − 70.

    Cao Hongyan, Chen Xizhang, Hu Chao, et al. Welding quality online detection based on infrared temperature measurement[J]. Journal of Shanghai Jiaotong University, 2016, 50(S1): 66 − 70.
    [31]
    张云舒, 邵丹丹, 丁东红, 等. 层间强制冷却对电弧熔丝增材制造钛合金温度场和应力场的影响[J]. 电焊机, 2023, 53(2): 111 − 116.

    Zhang Yunshu, Hao Dandan, Ding Donghong, et al. Effect of active interpass cooling on temperature and thermal stress evolution of wire arc additively manufactured Ti6Al4V alloy[J]. Electric Welding Machine, 2023, 53(2): 111 − 116.
    [32]
    Chen Xi, Fu Youheng, Kong Fanrong, et al. An in-process multi-feature data fusion nondestructive testing approach for wire arc additive manufacturing[J]. Rapid Prototyping Journal, 2022, 28(3): 573 − 584. doi: 10.1108/RPJ-02-2021-0034
    [33]
    Gao Xiangdong, You Deyong, Katayama Seiji, et al. Infrared image recognition for seam tracking monitoring during fiber laser welding[J]. Mechatronics, 2012, 22(4): 370 − 380. doi: 10.1016/j.mechatronics.2011.09.005
    [34]
    Han Yanfei, Jia Chungbao, He Chen, et al. Investigation on the metal transfer and cavity evolution during submerged arc welding with X-ray imaging technology[J]. Metals, 2023, 13(11): 1865. doi: 10.3390/met13111865
    [35]
    Li Yuxing, Polden J, Pan Zengxi, et al. A defect detection system for wire arc additive manufacturing using incremental learning[J]. Journal of Industrial Information Integration, 2022, 27: 100291. doi: 10.1016/j.jii.2021.100291
    [36]
    Zhu Beibei, Xiong Jun. Increasing deposition height stability in robotic GTA additive manufacturing based on arc voltage sensing and control[J]. Robotics and Computer-Integrated Manufacturing, 2020, 65: 101977. doi: 10.1016/j.rcim.2020.101977
    [37]
    Chen Yang, Jiang Linzhao, Peng Yunchao, et al. Ultra-fast laser ultrasonic imaging method for online inspection of metal additive manufacturing[J]. Optics and Lasers in Engineering, 2023, 160: 107244. doi: 10.1016/j.optlaseng.2022.107244
    [38]
    丁东红, 黄荣, 张显程, 等. 电弧增材制造研究进展: 多源信息传感[J]. 焊接技术, 2022, 51(10): 1 − 20 + 113.

    Ding Donhong, Huang Rong, Zhang Xiancheng, et al. Research progress of wire arc additive manufacturing: Multi-source information sensing[J]. Welding Technology, 2022, 51(10): 1 − 20 + 113.
    [39]
    陈超. 基于IOT与MAS结构的智能化焊接制造过程监控及系统研究[D]. 上海: 上海交通大学, 2021.

    Chen Chao, Intelligent welding manufacturing process monitoring and system research based on IOT and MAS structure[D]. Shanghai: Shanghai Jiao Tong University, 2021.
    [40]
    Yu Rongwei, Tan Xiaxia, He Shen, et al. Monitoring of robot trajectory deviation based on multimodal fusion perception in WAAM process[J]. Measurement, 2024, 224: 113933. doi: 10.1016/j.measurement.2023.113933
    [41]
    Feng Yunhe, Chen Zongyao, Wang Dali, et al. Deep welding: A deep learning enhanced approach to GTAW using multisource sensing images[J]. IEEE Transactions on Industrial Informatics, 2019, 16(1): 465 − 474.
    [42]
    Jiao Wenhua, Wang Qiyue, Cheng Yongchao, et al. End-to-end prediction of weld penetration: A deep learning and transfer learning based method[J]. Journal of Manufacturing Processes, 2021, 63: 191 − 197. doi: 10.1016/j.jmapro.2020.01.044
    [43]
    徐东辉, 孟范鹏, 陈树君, 等. 基于深度学习的GMAW焊接缺陷在线监测[J]. 焊接学报, 2024, 45(3): 14 − 119 + 135 − 136. doi: 10.12073/j.hjxb.20230117002

    Xu Donghui, Meng Fanpeng, Chen Shujun, et al. GMAW welding defect on-line monitoring based on deep learning[J]. Transactions of the China Welding Institution, 2024, 45(3): 14 − 119 + 135 − 136. doi: 10.12073/j.hjxb.20230117002
    [44]
    Zhang Yanxi, You Deyong, Gao Xiangdong, et al. Welding defects detection based on deep learning with multiple optical sensors during disk laser welding of thick plates[J]. Journal of Manufacturing Systems, 2019, 51: 87 − 94. doi: 10.1016/j.jmsy.2019.02.004
  • Related Articles

    [1]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
    [2]CHEN Xin, PENG Yong, ZHOU Qi, GUO Shun. Device and method for real-time monitoring of electron beam welding process based on space charge collection[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(6): 148-152. DOI: 10.12073/j.hjxb.2019400170
    [3]GONG Jianfeng, LI Huizhi, LI Liqun, Wang Gang. Quality monitoring technology of laser welding process based on coaxial image sensing[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(1): 37-42. DOI: 10.12073/j.hjxb.2019400008
    [4]ZHAO Dawei, WANG Xinyang, WANG Yuanxun, YANG Hao, ZHANG Lei. Quality assessment using dynamic voltage characteristics in small scale resistance spot welding of titanium alloy[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2014, 35(1): 33-36.
    [5]JI Chuntao, LUO Xianxing, Deng Lipeng. Acquisition and analysis of resistance spot welding quality characteristics[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2009, (6): 43-46.
    [6]CAO Biao, YE Wei-yuan, HUANG Zeng-hao, ZENG Min. Intelligent quality monitor of inverted resistance spot welding of wire to phosphor-copper sheet in relay manufacturing[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2006, (9): 47-50,54.
    [7]XU Jun, LI Yong-bing, CHEN Guan-long. Welding quality real-time monitoring system for auto-body assembly[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2006, (4): 41-44.
    [8]ZHANG Zhong-dian, LI Dong-qing, TANG Da-ping, LI Xue-jun. Measures for decreasing errors of ANN models of spot welding quality monitor[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2004, (5): 113-116.
    [9]ZHANG Zhong-dian, LI Dong-qing, YIN Xiao-hui. Study on Spot Welding Quality Monitoring Models by Linear Regression Theory[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2001, (4): 31-35.
    [10]YE Feng, SONG Yong-lun, LI Di, CHEN Fu-gen. On-line Quality Monitoring in Robot Arc Welding Process[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2001, (1): 5-7.
  • Cited by

    Periodical cited type(2)

    1. 张超,周猛兵,崔雷,陶欣,王军,王伟,刘永长. 9Cr-1.5W-0.15Ta耐热钢搅拌摩擦焊焊缝组织和冲击性能分析. 焊接学报. 2024(04): 36-42+131 . 本站查看
    2. 王猛,张立平,赵琳瑜,吴军,熊然,蒙永胜,李军红. 增材制造和锻造TC11钛合金激光焊接头组织与力学性能. 焊接学报. 2023(10): 102-110+138-139 . 本站查看

    Other cited types(1)

Catalog

    Article views (243) PDF downloads (96) Cited by(3)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return