Abstract
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.