On-line monitoring of TIG welding quality of nuclear power plug tube based on arc spectrum

被引：0

作者：

Bai Z. ^{[1
,2
]}

Li Z. ^{[1
,2
]}

Zhang Z. ^{[1
,2
]}

Qin R. ^{[1
,2
]}

Zhang S. ^{[1
,2
]}

Xu Y. ^{[3
]}

Wen G. ^{[1
,2
]}

机构：

[1] National Key Laboratory of Aeronautical Power Systems and Plasma Technology, Xi'an Jiaotong University, Xi'an

[2] School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an

[3] Xi’an Thermal Power Research Institute Co., Ltd., Xi'an

来源：

Hanjie Xuebao/Transactions of the China Welding Institution | 2024年 / 45卷 / 05期

关键词：

arc spectroscopy; deep learning; online monitoring; principal component analysis; tungsten inert gas shielded welding;

D O I：

10.12073/j.hjxb.20230610002

中图分类号：

学科分类号：

摘要：

In order to monitor the quality of TIG welding for blocked tube welding of high-temperature gas-cooled reactor steam generators under the constraints of operation space and radiation environment, a real-time monitoring system based on a fiber optic spectrometer for TIG welding process was developed for monitoring the depth of penetration during welding. This study used the system to collect arc spectra and utilized Principal Component Analysis to obtain the spectral principal components of different weld penetration depths. An innovative ATT-L2R-BiLSTM deep learning model was proposed to achieve classification and recognition of weld penetration depth during blocked tube TIG welding. The results show that the model achieved an accuracy of 92.61% under laboratory conditions, which is 5.11% higher than that of the Bi-LSTM network. The model was tested and verified on a blocked tube verification platform for nuclear power steam generators, achieving an accuracy of 99.26%. Finally, deep mining of welding quality features and precise evaluation of weld penetration depth during TIG welding were achieved under incomplete spectral information. © 2024 Harbin Research Institute of Welding. All rights reserved.

引用

页码：8 / 19

页数：11

共 28 条

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