On Line Monitoring of Weld Defects for Short-Circuit Gas Metal Arc Welding Based on the Self-Organize Feature Map Neural Networks

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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5

Como, Italy

July 24-July 27

ISBN: 0-7695-0619-4

	ASCII Text	x
	Li Di, Song Yonglun, Ye Feng, "On Line Monitoring of Weld Defects for Short-Circuit Gas Metal Arc Welding Based on the Self-Organize Feature Map Neural Networks," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 5, pp. 5239, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000.

	BibTex	x
	@article{ 10.1109/IJCNN.2000.861464, author = {Li Di and Song Yonglun and Ye Feng}, title = {On Line Monitoring of Weld Defects for Short-Circuit Gas Metal Arc Welding Based on the Self-Organize Feature Map Neural Networks}, journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on}, volume = {5}, year = {2000}, issn = {1098-7576}, pages = {5239}, doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861464}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on TI - On Line Monitoring of Weld Defects for Short-Circuit Gas Metal Arc Welding Based on the Self-Organize Feature Map Neural Networks SN - 1098-7576 SP EP A1 - Li Di, A1 - Song Yonglun, A1 - Ye Feng, PY - 2000 KW - weld KW - quality KW - defect KW - SOM KW - neural networks KW - GMAW VL - 5 JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on ER -

Li Di, South-China University of Technology,

Song Yonglun, South-China University of Technology,

Ye Feng, South-China University of Technology,

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.861464

In this paper a method for automatic detection of weld defects of short-circuit gas metal arc welding has been presented. It is based on the extraction of arc signal features as well as classification of the obtained features using Self-Organize feature Map (SOM) neural networks in order to get the weld quality information, for example, to determine if there is defect in the product. This is important for the on-line monitoring of weld quality especially in Robotic welding and lay the foundation for the further real-time control of weld quality.

Index Terms:

weld, quality, defect, SOM, neural networks, GMAW

Citation:

Li Di, Song Yonglun, Ye Feng, "On Line Monitoring of Weld Defects for Short-Circuit Gas Metal Arc Welding Based on the Self-Organize Feature Map Neural Networks," ijcnn, vol. 5, pp.5239, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5, 2000

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