Gas metal arc welding of butt joint with varying gap width based on neural networks

This paper describes the application of the neural network technology for gas metal arc welding (GMAW) control. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a certain degree of quality in the field of butt joint welding with full penetration, when the gap width is varying during the welding process.

The process modeling to facilitate the mapping from joint geometry and reference weld quality to significant welding parameters, has been based on a multi-layer feed-forward network. The Levenberg-Marquardt algorithm for non-linear least squares has been used with the back-propagation algorithm for training the network, while a Bayesian regularization technique has been successfully applied for minimizing the risk of inexpedient over-training.

Finally, a predictive closed-loop control strategy based on a so-called single-neuron self-learning PSD control algorithm has been proposed.