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| Citation: |
SUN Qian, HUANG Ruisheng, XU Fujia, CAO Hao, LI Lin, SONG Yang, MA Qiang. Study on physical characteristics of laser welding penetration signal in the mesoscopic field[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(7): 27-33, 40. DOI: 10.12073/j.hjxb.20230918001
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Reliable online penetration detection is an important issue in the intelligent manufacturing of laser welding, but the penetration signal belongs to the mesoscopic scale optical signal, and traditional detection methods are constrained by macroscopic sampling methods, which cannot effectively capture mesoscopic penetration information. Therefore, it is of great significance to carry out a study of the deeper mesoscopic properties within the laser keyhole to achieve reliable online penetration detection. In this paper, a mesoscopic penetration signal in the infrared spectrum was identified using a specialized optical imaging system. The study revealed that this signal possesses distinct physical characteristics, including strong concealment, high directionality, large fluctuation, and significant trend. Especially, it has been fully proven that the trend characteristics of the mesoscopic penetration signal under complex fluctuating surface. Through the abstract extraction and comparison analysis, it was found that the features are not only repeatable and subject to the nature of welding thermal reactions but also in good agreement with the laser welding penetration state. This study can provide crucial theoretical support for the development of reliable online detection of laser penetration at the mesoscopic scale.
| [1] |
John S, Alexios P, Panagiotis S. Quality assessment in laser welding: a critical review[J]. The International Journal of Advanced Manufacturing Technology, 2017, 94: 1825 − 1847.
|
| [2] |
Cai Wang, Wang Jianzhuang, Jiang Ping, et al. Application of sensing techniques and artificial intelligence-based methods to laser welding real-time monitoring: A critical review of recent literature[J]. Journal of Manufacturing Systems, 2020, 57: 1 − 18. doi: 10.1016/j.jmsy.2020.07.021
|
| [3] |
Tang Zijue, Liu Weiwei, Zhang Nan, et al. Real–time prediction of penetration depths of laser surface melting based on coaxial visual monitoring[J]. Optics and Lasers in Engineering, 2020, 128: 106034. doi: 10.1016/j.optlaseng.2020.106034
|
| [4] |
Luo Yi, Zhu Liang, Han Jingtao, et al. Study on the acoustic emission effect of plasma plume in pulsed laser welding[J]. Mechanical Systems and Signal Processing, 2019, 124: 715 − 723. doi: 10.1016/j.ymssp.2019.01.045
|
| [5] |
Yusof M, Ishak M, Ghazali M. Weld depth estimation during pulse mode laser welding process by the analysis of the acquired sound using feature extraction analysis and artificial neural network[J]. Journal of Manufacturing Processes, 2021, 63: 163 − 178. doi: 10.1016/j.jmapro.2020.04.004
|
| [6] |
Huang Yiming, Hou Shuaishuai, Xu Shufeng, et al. EMD- PNN based welding defects detection using laser-induced plasma electrical signals[J]. Journal of Manufacturing Processes, 2019, 45: 642 − 651. doi: 10.1016/j.jmapro.2019.08.006
|
| [7] |
Cao Yue, Wang Zhijiang, Hu Shengsun, et al. Modeling of weld penetration control system in GMAW-P using NARMAX methods[J]. Journal of Manufacturing Processes, 2021, 65: 512 − 524. doi: 10.1016/j.jmapro.2021.03.039
|
| [8] |
Cheng Hao, Zhou Liangang, Li Qijun, et al. Effect of welding parameters on spatter formation in full-penetration laser welding of titanium alloys[J]. Journal of Materials Research and Technology, 2021, 15: 5516 − 5525. doi: 10.1016/j.jmrt.2021.11.006
|
| [9] |
Lei Zhenglong, Shen Jianxiong, Wang Qun, et al. Real-time weld geometry prediction based on multi-information using neural network optimized by PCA and GA during thin-plate laser welding[J]. Journal of Manufacturing Processes, 2019, 43: 207 − 217. doi: 10.1016/j.jmapro.2019.05.013
|
| [10] |
Gao Xiangdong, Li Zhuman, Wang Lin, et al. Detection of weld imperfection in high-power disk laser welding based on association analysis of multi-sensing features[J]. Optics & Laser Technology, 2019, 115: 306 − 315.
|
| [11] |
Zhang Yanxi, You Deyong, Gao Xiangdong, et al. Online monitoring of welding status based on a DBN model during laser welding[J]. Engineering, 2019, 5: 671 − 678. doi: 10.1016/j.eng.2019.01.016
|
| [12] |
Kim H, Nam K, Oh S, et al. Deep-learning-based real-time monitoring of full-penetration laser keyhole welding by using the synchronized coaxial observation method[J]. Journal of Manufacturing Processes, 2021, 68: 1018 − 1030. doi: 10.1016/j.jmapro.2021.06.029
|
| [13] |
Fan Xi'an, Gao Xiangdong, Zhang Nanfeng, et al. Monitoring of 304 austenitic stainless-steel laser-MIG hybrid welding process based on EMD-SVM[J]. Journal of Manufacturing Processes, 2022, 73: 736 − 747. doi: 10.1016/j.jmapro.2021.11.031
|
| [14] |
Cai Wang, Jiang Ping, Shu LeShi, et al. Real-time monitoring of laser keyhole welding penetration state based on deep belief network[J]. Journal of Manufacturing Processes, 2021, 72: 203 − 214. doi: 10.1016/j.jmapro.2021.10.027
|
| [15] |
Yusof M, Ishak M, Ghazali M. Classification of weld penetration condition through synchrosqueezed-wavelet analysis of sound signal acquired from pulse mode laser welding process[J]. Journal of Materials Processing Technology, 2020, 279: 116559. doi: 10.1016/j.jmatprotec.2019.116559
|
| [16] |
Wang Qiyue, Jiao Wenhua, Wang Peng, et al. A tutorial on deep learning-based data analytics in manufacturing through a welding case study[J]. Journal of Manufacturing Processes, 2021, 63: 2 − 13. doi: 10.1016/j.jmapro.2020.04.044
|
| [17] |
Zhang Z H, Li B, Zhang W F, et al. Real-time penetration state monitoring using convolutional neural network for laser welding of tailor rolled blanks[J]. Journal of Manufacturing Systems, 2020, 54: 348 − 360. doi: 10.1016/j.jmsy.2020.01.006
|
| [18] |
Wu Di, Hu Minghua, Huang Yiming, et al. In situ monitoring and penetration prediction of plasma arc welding based on welder intelligence-enhanced deep random forest fusion[J]. Journal of Manufacturing Processes, 2021, 66: 153 − 165. doi: 10.1016/j.jmapro.2021.04.007
|
| [19] |
Shevchik S, Le T, Meylan B, et al. Supervised deep learning for real-time quality monitoring of laser welding with X-ray radiographic guidance[J]. Scientific Reports, 2020, 59: 3538.
|
| [20] |
Schmidt L, Römer F, Böttger D, et al. Acoustic process monitoring in laser beam welding[J]. Procedia CIRP, 2020, 94: 763 − 768. doi: 10.1016/j.procir.2020.09.139
|
| [21] |
Gao Y F, Wang Q S, Xiao J H, et al. Penetration state identification of lap joints in gas tungsten arc welding process based on two channel arc sounds[J]. Journal of Materials Processing Technology, 2020, 285: 116762. doi: 10.1016/j.jmatprotec.2020.116762
|
| [22] |
Kos M, Arko E, Kosler H, et al. Penetration-depth control in a remote laser-welding system based on an optical triangulation loop[J]. Optics and Lasers in Engineering, 2021, 139: 106464. doi: 10.1016/j.optlaseng.2020.106464
|
| [23] |
Lednev. V. N, Sdvizhenskii. P. A., Stavertiy, et al. Online and in situ laser-induced breakdown spectroscopy for laser welding monitoring[J]. Spectrochimica Acta Part B: Atomic Spectroscopy, 2021, 175: 106032. doi: 10.1016/j.sab.2020.106032
|
| [24] |
孙谦, 黄瑞生, 雷振等. 激光焊接熔透特征信号同轴增效提取方法研究[J]. 光谱学与光谱分析, 2020, 40: 679 − 683.
Sun Qian, Huang Ruisheng, Lei Zhen, et al. Study on coaxial synergistic extraction method of laser welding penetration characteristic signal[J]. Spectroscopy and Spectral Analysis, 2020, 40: 679 − 683.
|
| [25] |
Wang Lin, Gao Xiangdong, Kong Fanrong. Keyhole dynamic status and spatter behavior during welding of stainless steel with adjustable-ring mode laser beam[J]. Journal of Manufacturing Processes, 2022, 74: 201 − 219. doi: 10.1016/j.jmapro.2021.12.011
|
| [26] |
Zou Jianglin, Zhu Baoqi, Zhang Gaolei, et al. Power density effect on the laser beam-induced eruption of spatters in fiber laser keyhole welding[J]. Optics & Laser Technology, 2022, 147: 107651.
|
| [27] |
Wan Zixuan, Wang Huiping, Li Jingjing, et al. Effect of beam oscillation frequency on spattering in remote laser stitch welding of thin-gage zinc-coated steel with keyhole penetration[J]. Journal of Materials Processing Technology, 2022, 302: 117428.
|
| [28] |
Wen Xianhai, Wu Di, Zhang Peilei, et al. Influence mechanism of the keyhole behavior on penetration depth by in-situ monitoring in pulsed laser welding of aluminum alloy[J]. Optik., 2021, 246: 167812. doi: 10.1016/j.ijleo.2021.167812
|
| [29] |
Cheng Hao, Zhou Liangang, Sun Jianqiu, et al. Processing modes in laser beam oscillating welding of Al-6Cu alloy[J]. Journal of Manufacturing Processes, 2021, 68: 1261 − 1270. doi: 10.1016/j.jmapro.2021.06.049
|
| [30] |
Xiao Xianfeng, Fu Yanshu, Ye Xiaojun, et al. Analysis of heat transfer and melt flow in conduction, transition, and keyhole modes for CW laser welding[J]. Infrared Physics & Technology, 2022, 120: 103996.
|
| [31] |
Cunningham R, Zhao C, Parab N, et al. Keyhole threshold and morphology in laser melting revealed by ultrahigh-speed x-ray imaging[J]. Science, 2019, 363: 849 − 852. doi: 10.1126/science.aav4687
|
| [32] |
孙谦, 黄瑞生, 李小宇, 等. 激光焊熔透特征混沌信号解析与多维复合识别研究[J]. 光谱学与光谱分析, 2020, 40: 1076 − 1081.
Sun Qian, Huang Ruisheng, Li Xiaoyu, et al. Study on chaotic signal analysis and multidimensional composite recognition of penetration characteristics in laser welding[J]. Spectroscopy and Spectral Analysis, 2020, 40: 1076 − 1081.
|
| [33] |
孙谦, 王旭友, 王威, 等. 激光焊接质量快速无损检测方法: 10459378.3[P]. 2016-01-20.
Sun Qian, Wang Xuyou, Wang Wei, et al. Fast and nondestructive detection method for Laser welding quality: 10459378.3[P]. 2016-01-20.
|
| [34] |
孙谦, 黄瑞生, 邹吉鹏, 等. 基于信号介观萃取与统计分析的Q235钢熔透识别方法[J]. 焊接学报, 2020, 41: 29 − 33. doi: 10.12073/j.hjxb.20200422001
Sun Qian, Huang Ruisheng, Zou Jipeng, et al. Study on penetration recognition method of laser welding for Q235 steel based on signal mesoscopic extraction and statistical analysis[J]. Transactions of the China Welding Institution, 2020, 41: 29 − 33. doi: 10.12073/j.hjxb.20200422001
|
| [35] |
孙谦, 王旭友, 王威, 等. 激光焊接质量在线检测方法: 10459376.4[P]. 2015-08-05.
Sun Qian, Wang Xuyou, Wang Wei, et al. Online detertion method for laser welding quality: 10459376.4[P]. 2015-08-05.
|
| [36] |
孙谦, 王旭友, 王威, 等. 激光焊接熔透在线检测方法: 10326537.6 [P]. 2019-03-22.
Sun Qian, Wang Wei, Wang Xuyou, et al. On-line penetration detertion method of laser welding: 10326537.6[P]. 2019-03-22.
|
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