On-line monitoring of TIG welding quality of nuclear power plug tube based on arc spectrum

BAI Zijian; LI Zhiwen; ZHANG Zhifen; QIN Rui; ZHANG Shuai; XU Yaowen; WEN Guangrui

doi:10.12073/j.hjxb.20230610002

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2024 > 45(5): 8-19. > DOI: 10.12073/j.hjxb.20230610002

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

Citation:

PDF (2838 KB)

On-line monitoring of TIG welding quality of nuclear power plug tube based on arc spectrum

BAI Zijian^{1, 2,},
LI Zhiwen^{1, 2},
ZHANG Zhifen^{1, 2, ,},
QIN Rui^{1, 2},
ZHANG Shuai^{1, 2},
XU Yaowen³,
WEN Guangrui^{1, 2}

1.
National Key Laboratory of Aeronautical Power Systems and Plasma Technology, Xi'an Jiaotong University, Xi'an, 710049, China
2.
School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China
3.
Xi’an Thermal Power Research Institute Co., Ltd., Xi'an, 710054, China

More Information

Received Date: June 09, 2023
Available Online: March 08, 2024

Graphical Abstract

Abstract

Abstract

In order to monitor the quality of TIG welding for blocked tube welding of high-temperature gas-cooled reactor steam generators under the constraints of operation space and radiation environment, a real-time monitoring system based on a fiber optic spectrometer for TIG welding process was developed for monitoring the depth of penetration during welding. This study used the system to collect arc spectra and utilized Principal Component Analysis to obtain the spectral principal components of different weld penetration depths. An innovative ATT-L2R-BiLSTM deep learning model was proposed to achieve classification and recognition of weld penetration depth during blocked tube TIG welding. The results show that the model achieved an accuracy of 92.61% under laboratory conditions, which is 5.11% higher than that of the Bi-LSTM network. The model was tested and verified on a blocked tube verification platform for nuclear power steam generators, achieving an accuracy of 99.26%. Finally, deep mining of welding quality features and precise evaluation of weld penetration depth during TIG welding were achieved under incomplete spectral information.
- arc spectroscopy,
- tungsten inert gas shielded welding,
- principal component analysis,
- online monitoring,
- deep learning

FullText(HTML)

References (28)

References

[1]	欧清扬. 核电蒸汽发生器换热管内壁残余应力测试技术应用研究[D]. 广州: 华南理工大学, 2019. Ou Qingyang, Application of residual stress test technology on inner wall of heat exchange tube of nuclear power steam generator [D]. Guangzhou: South China University of Technology, 2019.
[2]	刘冀. 表面状态对蒸汽发生器传热管材高温高压水腐蚀行为的影响[D]. 合肥: 中国科学技术大学, 2021. Liu Ji. Influence of surface state on high temperature and high pressure water corrosion behavior of steam generator heat transfer pipe [D]. Hefei: University of Science and Technology of China, 2021.
[3]	齐欣. 5A06铝合金变动送气TIG焊焊缝性能研究[D]. 哈尔滨: 哈尔滨理工大学, 2021. Qi Xin. Study on weld properties of 5A06 aluminum alloy with variable gas injection TIG welding [D]. Harbin: Harbin University of Science and Technology, 2021.
[4]	李振华. 核电蒸汽发生器传热管疲劳行为及损伤机理研究[D]. 北京: 北京科技大学, 2022. Li Zhenhua. Study on fatigue behavior and damage mechanism of heat transfer tube of nuclear power steam generator [D]. Beijing: University of Science and Technology Beijing, 2022.
[5]	李春凯, 席保龙, 石玗, 等. 氟化物活性TIG焊电弧特征的光谱分析[J]. 焊接学报, 2021(42): 54 − 58. doi: 10.12073/j.hjxb.20210201002 Li Chunkai, Xi Baolong, Shi Yu, et al. Spectroscopic analysis of arc characteristics in fluoride-activated TIG welding[J]. Transactions of the China Welding Institution, 2021(42): 54 − 58. doi: 10.12073/j.hjxb.20210201002
[6]	熊俊, 郑森木, 陈辉, 等. 电弧增材制造成形在线监测与控制研究进展及展望[J]. 电焊机, 2021, 51(8): 70 − 78. doi: 10.7512/j.issn.1001-2303.2021.07.13 Xiong Jun, Zheng Senmu, Chen Hui, et al. Research progress and prospect of on-line monitoring and control of arc additive manufacturing[J]. Electric Welding Machine, 2021, 51(8): 70 − 78. doi: 10.7512/j.issn.1001-2303.2021.07.13
[7]	Xia C, Pan Z, Fei Z, et al. Vision based defects detection for Keyhole TIG welding using deep learning with visual explanation[J]. Journal of Manufacturing Processes, 2020, 56: 845 − 855. doi: 10.1016/j.jmapro.2020.05.033
[8]	卢振洋, 宫兆辉, 闫志鸿, 等. 基于深度学习的TIG焊背部熔池检测和熔宽提取[J]. 北京工业大学学报, 2019, 46(9): 988 − 996. doi: 10.11936/bjutxb2018070033 Lu Zhenyang, Gong Zhaohui, Yan Zhihong, et al. Deep learning based weld pool detection and weld width extraction for TIG welding back[J]. Journal of Beijing University of Technology, 2019, 46(9): 988 − 996. doi: 10.11936/bjutxb2018070033
[9]	王良瑞. 核电厚壁管道全位置TIG焊熔透状态监测及视觉信息表征[D]. 上海: 上海交通大学, 2020. Wang Liangrui. Penetration state monitoring and visual information characterization of full-position TIG welding for thick-wall nuclear power pipeline [D]. Shanghai: Shanghai Jiao Tong University, 2020.
[10]	Górka J, Jamrozik W. Enhancement of imperfection detection capabilities in TIG welding of the infrared monitoring system[J]. Metals, 2021, 11: 41 − 42.
[11]	Zhang Z F, Wen G R, Chen S B. 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
[12]	Ren W, Wen G, Xu B, 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.
[13]	Madhvacharyula A S, Pavan A V S, Gorthi S, et al. In situ detection of welding defects: a review[J]. Welding in the World, 2022, 66(4): 611 − 628. doi: 10.1007/s40194-021-01229-6
[14]	张晋, 袁召, 陈立学, 等. 离子体发射光谱诊断[J]. 强激光与粒子束, 2021, 33(6): 120 − 125. Zhang Jin, Yuan Zhao, Chen Lixue, et al. Ion emission spectroscopy diagnosis[J]. High Power Laser and Particle Beams, 2021, 33(6): 120 − 125.
[15]	刘莹, 杨立军, 何天玺, 等. 药芯焊丝TIG焊电弧特性的光谱分析[J]. 光谱学与光谱分析, 2017, 37(7): 2171 − 2176. Liu Ying, Yang Lijun, He Tianxi, et al. Spectral analysis of TIG welding arc characteristics of flux-cored wire[J]. Spectroscopy and Spectral Analysis, 2017, 37(7): 2171 − 2176.
[16]	刘自刚, 梅亚泽, 张建峰, 等. 深熔TIG焊研究现状与展望[J]. 热加工工艺, 2023, 52(1): 6 − 11. Liu Zigang, Mei Yaze, Zhang Jianfeng, et al. Research status and prospect of deep penetration TIG welding[J]. Hot Working Technology, 2023, 52(1): 6 − 11.
[17]	Mirapeix J, Vila E, Valdiande J J, et al. Real-time detection of the aluminium contribution during laser welding of Usibor1500 tailor-welded blanks[J]. Journal of Materials Processing Technology, 2016, 235(9): 106 − 113.
[18]	Zhang Z, Chen H, Xu Y, et al. Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding[J]. Mechanical Systems & Signal Processing, 2015, 60: 151 − 165.
[19]	Palanco S, Klassen M, Skupin J, et al. Spectroscopic diagnostics on CW-laser welding plasmas of aluminum alloys[J]. Spectrochim Acta Part B At Spectrosc, 2001, 56: 651 − 659.
[20]	Nomura K, Yoshii K, Toda K, et al. 3D measurement of temperature and metal vapor concentration in MIG arc plasma using a multi-directional spectroscopic method [J]. Journal of Physics, D. Applied Physics: A Europhysics Journal, 2017, 42: 425205.
[21]	Song Lijun,Huang Wenkang,Han Xu,et al. Real-time composition monitoring using support vector regression of laser-induced plasma for laser additive manufacturing[J]. IEEE Transactions on Industrial Electronics, 2016, 64(1): 633 − 642.
[22]	Zhang Linjie, Bai Qinglin, Ning Jie, et al. A comparative study on the microstructure and properties of copper joint between MIG welding and laser-MIG hybrid welding[J]. Materials & Design, 2016, 110(15): 35 − 50.
[23]	Huang Y, Wu D, Lü N, et al. Investigation of porosity in pulsed GTAW of aluminum alloys based on spectral and X-ray image analyses[J]. Journal of Materials Processing Technology, 2017, 243: 365 − 373. doi: 10.1016/j.jmatprotec.2016.12.026
[24]	叶昕. 基于光谱分析的电弧焊接在线检测与评估方法研究[D]. 镇江: 江苏大学, 2020. Ye Xin. Research on online detection and evaluation method of arc welding based on spectral analysis [D]. Zhenjiang: Jiangsu University, 2020.
[25]	Zhang Z, Yang Z, Ren W, et al. Random forest-based real-time defect detection of Al alloy in robotic arc welding using optical spectrum[J]. Journal of Manufacturing Processes, 2019, 42: 51 − 59. doi: 10.1016/j.jmapro.2019.04.023
[26]	Huang Y, Zhao D, Chen H, et al. Porosity detection in pulsed GTA welding of 5A06 Al alloy through spectral analysis[J]. Journal of Materials Processing Technology, 2018, 259: 332 − 340. doi: 10.1016/j.jmatprotec.2018.05.006
[27]	张志芬, 杨哲, 任文静, 等. 电弧光谱深度挖掘下的铝合金焊接过程状态检测[J]. 焊接学报, 2019, 40(1): 12 − 25. doi: 10.12073/j.hjxb.2019400005 Zhang Zhifen, Yang Zhe, Ren Wenjing, et al. Welding state detection of aluminum alloy by arc spectral deep mining[J]. Transactions of the China Welding Institution, 2019, 40(1): 12 − 25. doi: 10.12073/j.hjxb.2019400005
[28]	张萌. 基于AM_BiLSTM的在线评论质量分类研究[D]. 北京: 北京交通大学, 2021. Zhang Meng. Research on quality classification of online reviews based on AM_BiLSTM [D]. Beijing: Beijing Jiaotong University, 2021.

[1]	GUO Xiao, XU Kai, HUO Shubin, CHEN Peiyin, CHEN Bo. Investigation on the solidification segregation behavior of GTAW nickel alloy deposited metal[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(7): 105-108. DOI: 10.12073/j.hjxb.2019400190
[2]	FANG Kun, SONG Kuijing, Liang Ning, YANG Jianguo, FANG Hongyuan. Effect of welding segregation on microstructure and mechanical properties of regenerated weld for nanostructured bainite steel[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2016, 37(3): 71-74.
[3]	YE Xin, HUA Xueming, WU Yixiong. Microstructure evolution of HAZ of TIG welded Ni-based 718 superalloy[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(8): 97-100.
[4]	FANG Kun, SONG Kuijing, YANG Jianguo, LIU Xuisong, ZHAO Delong, FANG Hongyuan. Microstructure and properties of nanobainite steel joint by TIG welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2013, (8): 13-16.
[5]	QI Yanchang, PENG Yun, WEI Jinshan, TIAN Zhiling. Effect of carbon on microstructure and properties of C-1.5Mn-2.5Ni-0.5Cr-0.5Mo high strength steel weld metal[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (11): 41-44.
[6]	LI Chao, LIANG Yuanyuan, SHEN Canduo, ZHU Sheng. Cracking formation mechanism of mild steel parts fabricated by surfacing rapid forming[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (9): 53-56.
[7]	ZHOU Chengshuang, ZHENG Shuqi, CHEN Changfeng, CHEN Song. Influence of cell segregation inclusion of alloying elements on initiation of hydrogen blistering in heat-affected zone of acicular ferrite steels[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (6): 5-8.
[8]	CHEN Xue-ding, SONG Qiang, YU Wei-yuan, SONG Chong-yang. Corrosion of brazed joints with aluminum-based filler metals in vacuum[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (1): 40-43.
[9]	Lu Wenjiang, Yaoshikuni Nakao, Kenji Shinozaki. Susceptibility of liquation crack in HAZ of nickel base alloys[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1993, (3): 186-194.
[10]	Chen Xiaofeng, Si Zhongyao, Li Zhongku, Xiao Youhong. THE EFFECT OF DISTRIBUTION OF SOLUTE CONCENTRATION ON THE SOLIDIFICATION AND CRYSTALLIZATION IN THE WELD POOL[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1987, (1): 45-51.

Cited By

Cited by

Periodical cited type(12)

1.	周浩南，孙文磊，王伟，张志虎. 面向涂层裂纹的激光熔覆预测模型研究. 热加工工艺. 2024(14): 27-32 .
2.	郑世茂，刘玉国，王豪，王新佩，陈洪堂. 长直焊缝自动焊接设备研究. 南方农机. 2023(10): 127-128+154 .
3.	王颖，高胜，吴立明. 基于胶囊网络的TIG熔透预测. 焊接. 2023(04): 15-20+28 .
4.	黄威威，游德勇，高向东，张艳喜，黄宇辉. 基于相关分析和神经网络的激光焊接稳态识别. 激光技术. 2022(03): 312-319 .
5.	吴月玉，张弓，林群煦，侯至丞，杨文林. 机器人TIG焊接的焊缝形貌遗传神经网络预测. 制造业自动化. 2022(07): 86-90 .
6.	刘秀航，黄宇辉，张艳喜，高向东. 基于BP神经网络补偿卡尔曼滤波的激光-MIG复合焊缝熔宽在线检测. 中国激光. 2022(16): 115-121 .
7.	陶永，兰江波，任帆，王田苗，江山，高赫，温宇方. 基于自适应模糊神经网络的机器人焊接焊缝外形预测方法. 计算机集成制造系统. 2022(11): 3643-3651 .
8.	刘天元，鲍劲松，汪俊亮，顾俊. 融合时序信息的激光焊接熔透状态识别方法. 中国激光. 2021(06): 228-238 .
9.	吴月玉，张弓，林群煦，侯至丞，杨文林，刘胜祥，徐群华，张雨航. 焊接机器人特征参数预测方法的研究综述与展望. 机床与液压. 2021(15): 168-173+199 .
10.	成慧翔，马艳娥，李新卫. 基于改进神经网络的激光焊接偏差智能识别研究. 激光杂志. 2021(12): 165-169 .
11.	火巧英，闫海宁，涂本荣，陆安进. 焊接工艺参数对Q345NQR2耐候钢激光焊焊缝成形的影响. 焊接技术. 2020(08): 16-18+105-106 .
12.	范鹏飞，张冠. 基于线性回归和神经网络的金属陶瓷激光熔覆层形貌预测. 表面技术. 2019(12): 353-359+368 .

Other cited types(6)

Get Citation

PDF

XML

Article views (214) PDF downloads (60) Cited by(18)

Turn off MathJax

Article Contents

Abstract

References

On-line monitoring of TIG welding quality of nuclear power plug tube based on arc spectrum