Unstable state vision detection of molten pool during aluminum alloy climbing-TIG welding

HONG Yuxiang; YANG Mingxuan; DU Dong; CHANG Baohua; XIAO Hong

doi:10.12073/j.hjxb.20201208001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2021 > 42(10): 8-13. > DOI: 10.12073/j.hjxb.20201208001

HONG Yuxiang, YANG Mingxuan, DU Dong, CHANG Baohua, XIAO Hong. 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. DOI: 10.12073/j.hjxb.20201208001

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Unstable state vision detection of molten pool during aluminum alloy climbing-TIG welding

1.
Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou, 310018, China
2.
Key Laboratory for Advanced Materials Processing Technology, Ministry of Education, Tsinghua University, Beijing, 100084, China
3.
Tianjin Aerospace Long March Rocket Manufacturing Co. Ltd., Tianjin, 300462, China

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Received Date: December 07, 2020
Available Online: November 15, 2021

Graphical Abstract

Abstract

Abstract

Visual detection of the state of the molten pool during the welding process is an important means to realize the online monitoring of weld quality. Aiming at the problems of molten pool unstable state and forming defects that are likely to occur during the climbing tungsten helium arc welding process of medium and thick aluminum alloys, this paper proposes a Tungsten Inert Gas Welding(TIG) welding status monitoring method based on the image characteristics of the molten pool. Based on the constructed passive vision sensor system, the acquisition of clear images of the molten pool under the interference of strong arc light is realized. A helium arc welding based on Otsu’s threshold segmentation and visual saliency features(VSF) is proposed. The image processing algorithm of the molten pool is used to extract the morphological features of the molten pool, and the relationship between the extracted visual features and the stability of the aluminum alloy climbing-TIG welding process is analyzed. Finally, a support vector machine (SVM) model is established to identify the welding state. The experimental results show that, compared with the geometric characteristics of the molten pool contour, the morphological characteristics of the molten metal at the end of the molten pool can more effectively reflect the unstable state of the molten pool during the aluminum alloy climbing-TIG welding process. The established welding state classification model has a maximum accuracy of 95.94% under the condition of a single feature input. The proposed real-time detection method provides a basis for online intelligent diagnosis and process optimization of TIG weld forming defects of large aluminum alloy components.
- climbing-TIG welding,
- medium and thick plate aluminum alloy,
- welding process detection,
- visual sensing,
- molten pool features

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References

赵红星, 王国庆, 杨春利, 等. 氦弧与氩弧电弧特性对比研究[J]. 机械工程学报, 2018, 54(8): 137 − 143. doi: 10.3901/JME.2018.08.137

Zhao Hongxing, Wang Guoqing, Yang Chunli, et al. Comparative research of helium and argon arc characters[J]. Journal of Mechanical Engineering, 2018, 54(8): 137 − 143. doi: 10.3901/JME.2018.08.137

Wang Y J, Yu C, Lu H, et al. Research status and future perspectives on ultrasonic arc welding technique[J]. Journal of Manufacturing Processes, 2020, 58: 936 − 954. doi: 10.1016/j.jmapro.2020.09.005

张志芬, 张林杰, 杨哲, 等. 航空航天用铝合金机器人焊接内部气孔缺陷在线检测[J]. 航空制造技术, 2019, 62(Z2): 14 − 24.

Zhang Zhifen, Zhang Linjie, Yang Zhe, et al. On-line inner porosity defect detection of aluminum alloy robotic welding for aerospace[J]. Aerospace Manufacturing Technology, 2019, 62(Z2): 14 − 24.

Huang Y M, Yuan Y X, Yang L J, et al. A study on porosity in gas tungsten arc welded aluminum alloys using spectral analysis[J]. Journal of Manufacturing Processes, 2020, 57: 334 − 343. doi: 10.1016/j.jmapro.2020.06.033

Zhang Z F, Wen G R, Chen S B. Audible sound-based intelligent evaluation for aluminum alloy in robotic pulsed GTAW: mechanism, feature selection, and defect detection[J]. IEEE Transactions on Industrial Informatics, 2018, 14(7): 2973 − 2983. doi: 10.1109/TII.2017.2775218

Chen Z Y, Chen J, Feng Z L. Welding penetration prediction with passive vision system[J]. Journal of Manufacturing Processes, 2018, 36: 224 − 230. doi: 10.1016/j.jmapro.2018.10.009

Qi Jiyang, Li Jinyan. Feature extraction of welding defect based on machine vision[J]. China Welding, 2019, 28(1): 56 − 62.

李鹤喜, 韩新乐, 方灶军. 一种基于CNN深度学习的焊接机器人视觉模型[J]. 焊接学报, 2019, 40(2): 154 − 160.

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.

夏卫生, 龚福建, 杨荣国, 等. 基于红外视觉的熔化极气体保护焊外观缺陷识别[J]. 焊接学报, 2020, 41(3): 69 − 73.

Xia Weisheng, Gong Fujian, Yang Rongguo, et al. Apparent defect recognition of gas metal arc welding based on infrared vision[J]. Transactions of the China Welding Institution, 2020, 41(3): 69 − 73.

肖宏, 宋建岭, 常保华, 等. 基于形态学算法的2219铝合金钨极氦弧焊熔池图像特征提取[J]. 宇航材料工艺, 2019, 49(1): 78 − 81. doi: 10.12044/j.issn.1007-2330.2019.01.015

Xiao Hong, Song Jianling, Chang Baohua, et al. Image feature extraction of helium gas tungsten arc welding pool of 2219 aluminum alloy based on morphological algorithm[J]. Aerospace Materials & Technology, 2019, 49(1): 78 − 81. doi: 10.12044/j.issn.1007-2330.2019.01.015

Peng G D, Gao Y J, Tian Z J, et al. Penetration control of GTAW process for aluminum alloy using vision sensing[J]. Journal of Physics: Conference Series, 2019, 1303: 012139. doi: 10.1088/1742-6596/1303/1/012139

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Unstable state vision detection of molten pool during aluminum alloy climbing-TIG welding