Real-time recognition of weld defects based on visible spectral image and machine learning

Sujie Zhang; Ming Deng; Xiaoyuan Xie

doi:10.1051/matecconf/202235503014

All issues

Volume 355 (2022)

MATEC Web Conf., 355 (2022) 03014

References

Open Access

Issue		MATEC Web Conf. Volume 355, 2022 2021 International Conference on Physics, Computing and Mathematical (ICPCM2021)


Article Number		03014
Number of page(s)		7
Section		Computing Methods and Computer Application
DOI		https://doi.org/10.1051/matecconf/202235503014
Published online		12 January 2022

Jeffus LF. Welding: principles and applications. Thomson/Delmar Learning; 2004 [Google Scholar]
Xu Zhenjie. On-line Spectral Diagnosis of Arc Characteristics and Air Bore in Aluminum Alloy Arc Additive Manufacturing Process. Huazhong University of Science and Technology, 2016 [Google Scholar]
Lucas W. Real-time vision-based control of weld pool size. Weld Int 2012;26(4):243–50 [Google Scholar]
Yu J. The development of a quality prediction system for aluminum laser welding to measure plasma intensity using photodiodes. Journal of Mechanical Science and Technology, 2016, 30(10):4697-4704. [Google Scholar]
Gu H. Resonant acoustic emission during laser welding of metals. Journal of Physics D Applied Physics, 1999, 29(3): 550. [Google Scholar]
Mirapeix J. Real-time arc welding defect detection technique by means of plasma spectrum optical analysis. NDT & E international: Independent nondestructive testing and evaluation, 2006, 39(5): 356-360. [Google Scholar]
Daniel Bacioiua. Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning. NDT & E International, 2019, 102139 [Google Scholar]
Yakowitz S. Nearest-neighbour methods for time series analysis. Journal of Time Series Analysis, 1987, 8(2): 235－247 [Google Scholar]
Lecun Y. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11):2278-2324. [Google Scholar]
Kingma DP. Adam: a method for stochastic optimization. In: Proceedings of the 2015 International Conference on Learning Representations; 2015 May7–9; San Diego, CA, USA; 2015. [Google Scholar]

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[1] Jeffus LF. Welding: principles and applications. Thomson/Delmar Learning; 2004 [Google Scholar]

[2] Xu Zhenjie. On-line Spectral Diagnosis of Arc Characteristics and Air Bore in Aluminum Alloy Arc Additive Manufacturing Process. Huazhong University of Science and Technology, 2016 [Google Scholar]

[3] Lucas W. Real-time vision-based control of weld pool size. Weld Int 2012;26(4):243–50 [Google Scholar]

[4] Yu J. The development of a quality prediction system for aluminum laser welding to measure plasma intensity using photodiodes. Journal of Mechanical Science and Technology, 2016, 30(10):4697-4704. [Google Scholar]

[5] Gu H. Resonant acoustic emission during laser welding of metals. Journal of Physics D Applied Physics, 1999, 29(3): 550. [Google Scholar]

[6] Mirapeix J. Real-time arc welding defect detection technique by means of plasma spectrum optical analysis. NDT & E international: Independent nondestructive testing and evaluation, 2006, 39(5): 356-360. [Google Scholar]

[7] Daniel Bacioiua. Automated defect classification of SS304 TIG welding process using visible spectrum camera and machine learning. NDT & E International, 2019, 102139 [Google Scholar]

[8] Yakowitz S. Nearest-neighbour methods for time series analysis. Journal of Time Series Analysis, 1987, 8(2): 235－247 [Google Scholar]

[9] Lecun Y. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11):2278-2324. [Google Scholar]

[10] Kingma DP. Adam: a method for stochastic optimization. In: Proceedings of the 2015 International Conference on Learning Representations; 2015 May7–9; San Diego, CA, USA; 2015. [Google Scholar]