Porosity Prediction by Emission Spectra During Narrow Gap Laser Wire Filling Welding
SHE Kun1, 2, LI Dong-hui1, YANG Kai-song3, YANG Li-jun3*, LIU Jin-ping4, HUANG Yi-ming3*
1. School of Electrical and Information Engineering, Tianjin University, Tianjin 300350, China
2. Guangdong Institute of Special Equipment Inspection and Research, Guangzhou 510655, China
3. Tianjin Key Laboratory of Advanced Joining Technology, School of Materials Science and Engineering, Tianjin University, Tianjin 300350, China
4. China Nuclear Industry 23 Construction Co., LTD., Industry Research and Engineering Co., LTD., China National Nuclear Corporation Key Laboratory of High Efficiency Welding, Beijing 101300, China
Abstract：As an advanced joining process for large thick components in nuclear power fields, narrow gap laser welding has the advantages of low heat input, high welding efficiency, and high joint quality. However, attributing to the complex welding environment on site, porosity defects are prone to be generated due to inadequate cleaning of pollutants. The traditional post-welding nondestructive testing is time and labor-consuming, and the part size and the subjective judgment of the testers restrict the test results. Therefore, developing an online detection technology for porosity defects is urgent. In this study, a narrow gap laser welding detection system based on the emission spectrum was designed and developed. The effect of process parameters and pollutants such as water and oil on welding quality was investigated. The action mechanism of water on the electron temperature and spectral intensity of laser-induced plasma was analyzed. An online warning software system for porosity defects caused by pollutants was developed. The results showed that the spectrum intensity of narrow gap laser welding was weak due to the strong reflection and scattering of high plasma density caused by the side wall constraint of the workpiece. Due to the loss of laser energy, the measured spectral intensity during wire-filling welding was less than that of self-fusion welding. The electron temperature and electron density of plasma induced by narrow gap laser filling wire welding were 7 201.1 K and 5.279 7×1015 cm-3, respectively, which were both lower than the thermodynamic parameters of self-fusion welding. Dense porosity defects were not detected by X-ray inspection in the self-fusion welding. When water was on the base material surface, pores on the weld surface were observed, and many dense pores were detected by X-ray inspection. The relative light intensity in all bands was reduced compared with the spectral data obtained under the normal process. The electron temperature also reduced from 6 900 to 7 200 K, but the electron density increased. Using a neural network model to train the spectral data after dimensionality reduction of principal component analysis, the porosity defects caused by water and oil in narrow gap laser wire filling welding can be predicted with high accuracy. The developed detection system can effectively identify porosity defects caused by pollutants with an accuracy of 90 % and a response time of 0.1 s.
佘 昆，李冬辉，杨凯淞，杨立军，刘金平，黄一鸣. 基于光谱诊断的窄间隙激光填丝焊气孔缺陷检测研究[J]. 光谱学与光谱分析, 2025, 45(02): 507-514.
SHE Kun, LI Dong-hui, YANG Kai-song, YANG Li-jun, LIU Jin-ping, HUANG Yi-ming. Porosity Prediction by Emission Spectra During Narrow Gap Laser Wire Filling Welding. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2025, 45(02): 507-514.
[1] Ramakrishna R V，Amrutha P，Rashid R, et al. The International Journal of Advanced Manufacturing Technology, 2020, 111(7-8): 2277.
[2] Li R，Wang T，Wang C，et al. Optics & Laser Technology, 2014, 64: 172.
[3] Elmesalamy A, Francis J A, Li L. International Journal of Pressure Vessels and Piping，2014, 113：49.
[4] Huang Y，Hou S，Xu S，et al. Journal of Manufacturing Processes, 2019, 45: 642.
[5] Ning J, Zhang L J, Yang J N, et al. Journal of Materials Processing Technology, 2019, 270: 168.
[6] Zhang Z, Zhang L, Wen G. Journal of Manufacturing Processes, 2019, 39: 79.
[7] Dong J, Huang Y, Zhu J, et al. Materials, 2023, 16(3): 1312.
[8] Wu D, Zhang P, Yu Z, et al. Journal of Manufacturing Processes, 2022, 75: 767.
[9] Buongiorno D, Prunella M, Grossi S, et al . Applied Sciences, 2022, 12(13): 6455.
[10] Will T, Jeron T, Hoelbling C, et al. Micromachines, 2022, 13(11): 1937.
[11] Huang W, Gao X, Huang Y, et al. International Journal of Precision Engineering and Manufacturing, 2023, 24(1): 33.
[12] Zhao S, Yang L, Huang Y, et al. The International Journal of Advanced Manufacturing Technology, 2019, 104: 893.
[13] Huang Y, Xu S, Yang L, et al. Journal of Materials Processing Technology, 2019, 271: 394.
[14] Cai W, Wang J, Jiang P, et al. Journal of Manufacturing Systems, 2020, 57: 1.
[15] Chen Y, Chen B, Yao Y, et al. NDT & E International, 2019, 108: 102176.
[16] Lednev V N, Sdvizhenskii P A, Stavertiy A Y, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2021, 175: 106032.
[17] Huang Y, Wu D, Zhang Z, et al. Journal of Materials Processing Technology, 2017, 239: 92.
