Welding current and voltage detection and control method based on visual sensing

LE Jian; LI Fayuan; SHU Zhiheng; ZENG Mingru; ZHANG Hua

doi:10.12073/j.hjxb.20240705002

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2024 > 45(11): 85-89. > DOI: 10.12073/j.hjxb.20240705002

LE Jian, LI Fayuan, SHU Zhiheng, ZENG Mingru, ZHANG Hua. Welding current and voltage detection and control method based on visual sensing[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(11): 85-89. DOI: 10.12073/j.hjxb.20240705002

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Welding current and voltage detection and control method based on visual sensing

1.
School of Information Engineering, Nanchang University, Nanchang, 330031, China
2.
Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components, Shanghai University of Engineering Science, Shanghai, 201620, China

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Received Date: July 04, 2024
Available Online: October 18, 2024

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Abstract

Abstract

Welding current and voltage are important parameters that affect welding quality. In order to improve welding quality, a method of on-line detection and accurate control of welding current and voltage based on visual sensing technology has been studied in this paper. Firstly, the preprocessing of welding current and voltage image is studied, and the character image corresponding to welding current and voltage is segmented. Then, a recognition method of corresponding characters of welding current and voltage based on projection feature is studied, and the recognition of welding current and voltage is further realized. Finally, the automatic control method of welding current and voltage based on expert control is studied, which can improve the stability, accuracy and rapidity of automatic regulation of welding current and voltage. The experimental results show that using the method studied in this paper, the accuracy of online identification of welding current and voltage is higher than 99.75%. It can achieve automatic detection and control of welding current and voltage before and during welding, and can adjust the weld bead size online as needed, which helps to improve the automation level and welding quality.
- vision sensing technology,
- welding current and voltage,
- online detection,
- automatic control,
- welding automation

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References (10)

References

[1]	Le J, Li F Y, Zeng M R, et al. Research on the online detection method of fillet welding arc stability based on the adaptive model of rotating arc stability sensor[J]. Mechanical Systems and Signal Processing, 2024, 217: 111500. doi: 10.1016/j.ymssp.2024.111500
[2]	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
[3]	徐东辉, 孟范鹏, 孙鹏, 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): 114 − 119. doi: 10.12073/j.hjxb.20230117002 Xu Donghui, Meng Fanpeng, Sun Peng, et al. 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
[4]	徐远钊, 罗玖田, 方乃文, 等. 基于MS-FCM算法的船体板熔池图像处理技术[J]. 焊接学报, 2024, 45(3): 82 − 90. doi: 10.12073/j.hjxb.20231010001 Xu Yuanzhao, Luo Jiutian, Fang Naiwen, et al. Image processing technology for ship plate melt pool based on MS - FCM algorithm[J]. Transactions of the China Welding Institution, 2024, 45(3): 82 − 90. doi: 10.12073/j.hjxb.20231010001
[5]	王颖, 高胜. 基于改进CeiT的GTAW焊接熔透状态识别方法[J]. 焊接学报, 2024, 45(4): 26 − 35. doi: 10.12073/j.hjxb.20230327002 Wang Ying, Gao Sheng. Identification method of GTAW welding penetration state based on improved CeiT[J]. Transactions of the China Welding Institution, 2024, 45(4): 26 − 35. doi: 10.12073/j.hjxb.20230327002
[6]	Aldalur E, Suárez A, Curiel D, et al. Intelligent and adaptive system for welding process automation in T-shaped joints[J]. Metals, 2023, 13(9): 1532. doi: 10.3390/met13091532
[7]	Sonwane S, Chiddarwar S. Automatic weld joint type recognition in intelligent welding using image features and machine learning algorithms[J]. Edam-Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2024, 37: e26.
[8]	Lu J, Yang A D, Chen X Y, et al. A seam tracking method based on an image segmentation deep convolutional neural network[J]. Metals, 2022, 12(8): 1365. doi: 10.3390/met12081365
[9]	郭忠峰, 刘俊池, 杨钧麟. 基于关键点检测方法的焊缝识别[J]. 焊接学报, 2024, 45(1): 88 − 93. doi: 10.12073/j.hjxb.20230204001 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
[10]	Kang Y H, Chen R S. Welding robot automation technology based on digital twin[J]. Frontiers in Mechanical Engineering, 2024, 10: 1367690. doi: 10.3389/fmech.2024.1367690

[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.

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Welding current and voltage detection and control method based on visual sensing