Spatter analysis of rotating arc image based on multi threshold and neural network

TANG Quan; SHI Zhixin; MAO Zhiwei

doi:10.12073/j.hjxb.20211219001

TRANSACTIONS OF THE CHINA WELDING INSTITUTION > 2022 > 43(12): 41-46. > DOI: 10.12073/j.hjxb.20211219001

TANG Quan, SHI Zhixin, MAO Zhiwei. Spatter analysis of rotating arc image based on multi threshold and neural network[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2022, 43(12): 41-46. DOI: 10.12073/j.hjxb.20211219001

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Spatter analysis of rotating arc image based on multi threshold and neural network

Nanchang University, Nanchang, 330031, China

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Received Date: December 18, 2021
Available Online: December 07, 2022

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Abstract

Abstract

To exploring the causes and rules of rotary arc spatter, a combination method of multi threshold and BP neural network based on mask was proposed to identify welding spatter in accordance with the welding images of rotary arc flat surfacing collected by high-speed camera. The multi threshold method was used to obtain the spatter position and contour, and then the spatter was identified by establishing a BP neural network model with five characteristic values The recognition accuracy of this combined method can reach 95.76% for rotating arc spatter images with complex background. At the same time, through the phase analysis of spatter and welding wire position, the average phase of the maximum number of spatters is 241.4°, that is, about 0.14 cycle position after the end of welding wire enters the molten pool. This is mainly due to the current surge caused by the contact between the droplet at the end of welding wire and the molten pool, and the insufficient current suppression, The research results provide a basis for controlling spatter in rotating arc welding.
- rotating arc,
- welding spatter,
- multi threshold processing,
- BP neural network,
- image processing

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References

You D, Gao X, Katayama S. Monitoring of high-power laser welding using high-speed photographing and image processing[J]. Mechanical Systems and Signal Processing, 2014, 49(1/2): 39 − 52.

刘西洋, 徐锴, 杨淼森, 等. 基于表面张力的氧化物组分及含量对焊接飞溅的影响[J]. 焊接, 2020(8): 22 − 25.

Liu Xiyang, Xu Kai, Yang Miaosen, et al. Effect of component and content of oxide based on surface tension on welding spatter[J]. Welding & Joining, 2020(8): 22 − 25.

张恒铭, 石玗, 李春凯, 等. 极性对细直径自保护药芯焊丝CMT下熔滴过渡及焊缝成形的影响[J]. 焊接学报, 2021, 42(8): 75 − 81. doi: 10.12073/j.hjxb.20210419001

Zhang Hengming, Shi Yu, Li Chunkai, et al. Effect of polarity on droplet transfer and weld formation of fine diameter self shielded flux cored wire under CMT[J]. Transactions of the China Welding Institution, 2021, 42(8): 75 − 81. doi: 10.12073/j.hjxb.20210419001

Lü Xiaoqing, Cao Biao, Zeng Min, et al. Effects of current waveform parameters during droplet transfer on spatter in high speed waveform controlled Short-circuiting GMAW[J]. China Welding, 2005, 14(2): 121 − 124.

Kang S, Kang M, Jang Y H, et al. Droplet transfer and spatter generation in DC-AC pulse tandem gas metal arc welding[J]. Science and Technology of Welding and Joining, 2020, 25(7): 589 − 599. doi: 10.1080/13621718.2020.1786262

孔海旺, 李科, 王金波, 等. CO₂气体保护焊熔滴过渡与飞溅的研究[J]. 热加工工艺, 2017, 46(11): 235 − 237.

Kong Haiwang, Li Ke, Wang Jinbo, et al. Study on droplet transfer and spatter in CO₂ gas shielded arc welding[J]. Hot Working Technology, 2017, 46(11): 235 − 237.

Shareef I, Martin C. Effect of process parameters on weld spatter in robotic welding[C]//Procedia Manufacturing. 48th SME North American Manufacturing Research Conference. Cincinnati, Ohio, USA, 2020, 48: 358 − 371.

Haubold M W, Zäh M F. Real-time spatter detection in laser welding with beam oscillation[C]//Procedia CIRP. 12th CIRP Conference on Intelligent Computation in Manufacturing Engineering. Gulf of Naples, Italy, 2019, 79: 159 − 164.

Yang D, Li H, Liu S, et al. In situ capture of spatter signature of SLM process using maximum entropy double threshold image processing method based on genetic algorithm[J]. Optics & Laser Technology, 2020, 131(6): 106371.

Huang Y, Hua X, Li F, et al. Spatter feature analysis in laser welding based on motion tracking method[J]. Journal of Manufacturing Processes, 2020, 55(4): 220 − 229.

Tan Z, Fang Q, Li H, et al. Neural network based image segmentation for spatter extraction during laser-based powder bed fusion processing[J]. Optics & Laser Technology, 2020, 130(3): 106347.

Xia X, Jiang Z, Xu P. A detection algorithm of spatter on welding plate surface based on machine vision[J]. Optoelectronics Letters, 2019, 15(1): 52 − 56. doi: 10.1007/s11801-019-8104-7

Zhao Z, Deng L, Bai L, et al. Optimal imaging band selection mechanism of weld pool vision based on spectrum analysis[J]. Optics & Laser Technology, 2019, 110(9): 145 − 151.

陈明. MATLAB神经网络原理与实例精解[M]. 北京: 清华大学出版社, 2013.

Chen Ming. The principle of MATLAB neural network and the detailed explanation of examples[M]. Beijing: Tsinghua University Press, 2013.

[1]	YI Rongtao, ZHAO Dawei, WANG Yuanxun. Numerical simulation of resistance spot welding considering phase transition effect[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2013, (10): 71-74.
[2]	BAO Li, LONG Weimin, PEI Yinyin, MA Jia. Thermal kinetics for phase transformation of CuZnSnSi alloy[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2013, (10): 55-58.
[3]	ZHENG Huaibei, YE Xiaoning, ZHANG Xuefeng, JIANG Laizhu, LIU Zhenyu, WANG Guodong. Microstructure transformation,grain growth and precipitated phase of 12%Cr ferritic stainless steel in coarse grain zone[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2011, (6): 37-40.
[4]	YANG Guangchen, ZHANG Yanhua. Effect of high-temperature phase transition of low carbon steel on weld angular distortion[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (8): 65-67.
[5]	LIU Xin, GONG Shuili, LEI Yongping. Thermodynamic character of phase transformation of TC4 titanium alloy electron beam welded joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2010, (2): 57-59,98.
[6]	ZHANG Genyuan, XU Maili, TIAN Songya, Wen Fang. Genetic algorithm of grain growth in heat-affected zone of 45 steel AC flash butt welding[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2009, (6): 79-82.
[7]	QIN Bin, SHENG Guang-min, HUANG Jia-wei, LI Cong. Phase transformation diffusion bonding technology for titanium alloy to stainless steel[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2006, (1): 41-44,48.
[8]	WEN Jun qin, LIU Xin tian, MO Chun li, ZHANG Shi xing. Microstructure simulation of grain growth in heat affected zone[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2003, (3): 48-51.
[9]	Long Xin, Xu Yonggang, Cai Guangjun. Study on the Continuous Cooling Transformation of Modified 9%Cr-1%Mo Weld Metals[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2000, (1): 53-56.
[10]	Wang Yucheng, Yu Jie, Li Yan, Li Weidong, Wang Jianguang. Martensitic Transformation and Microstructure in Fusion Zone of Dissimilar Steel Welded Joint[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 1999, (3): 147-152.

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Spatter analysis of rotating arc image based on multi threshold and neural network