On line monitoring of burning through for short circuit CO2 arc welding based on the self-organize feature map neural networks

Abstract

A method for automatic detection of burning through of short circuit CO2 arc welding is 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.