Intelligent monitoring and recognition of the short-circuiting gas—metal arc welding process | Zendy

Chuansong Wu | Zendy; Qingxian Hu | Zendy; Jiamin Sun | Zendy; T. Polte | Zendy; D. Rehfeldt | Zendy

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Intelligent monitoring and recognition of the short-circuiting gas—metal arc welding process

Author(s) -

Chuansong Wu,

Qingxian Hu,

Jiamin Sun,

T. Polte,

D. Rehfeldt

Publication year - 2004

Publication title -

proceedings of the institution of mechanical engineers part b journal of engineering manufacture

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.861

H-Index - 64

eISSN - 2041-2975

pISSN - 0954-4054

DOI - 10.1243/0954405041897121

Subject(s) - welding , process (computing) , gas metal arc welding , artificial neural network , arc welding , fuzzy logic , cluster analysis , artificial intelligence , computer science , pattern recognition (psychology) , self organizing map , welding power supply , fuzzy clustering , arc (geometry) , engineering , mechanical engineering , filler metal , operating system

This paper introduces an intelligent system for monitoring and recognition of process disturbances during short-circuiting gas-metal arc welding. It is based on the measured and statistically processed data of welding electrical parameters. A 12-dimensional array of process features is designed to describe various welding conditions and is employed as input vector of the intelligent system. Three methods, such as fuzzy c-means, neural network and fuzzy Kohonen clustering network are used to conduct process monitoring and automatic recognition. The correct recognition rates of these three methods are compared.

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