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

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.