This paper presents results from an experimentally based research on Gas Metal Arc Welding (GMAW), controlled by the artificial neural network (ANN) technology. A system has been developed for modeling and online adjustment of welding parameters, appropriate to guarantee a high degree of quality in the challenging field of butt joint welding with full penetration under stochastically changing boundary conditions, e.g. major gap width variations. GMAW experiments performed on mild-steel plates (3 mm of thickness), show that high quality welds with uniform back-bead geometry are achievable for gap width variations from 0.5 mm to 2.3 mm - scanned 10 mm in front of the electrode location. In this research, the mapping from joint geometry and reference weld quality to significant welding parameters has been based on a static multi-layer feed-forward network. The Levenberg-Marquardt algorithm, for non-linear least square error minimization, 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.
|Title of host publication||Procedings of 13th International Conference on Computer Technology in Welding|
|Publisher||National Institute of Standards and Technology|
|Publication status||Published - 2004|
Christensen, K. H., & Sørensen, T. (2004). Control of GMA Butt Joint Welding Based on Neural Networks. In Procedings of 13th International Conference on Computer Technology in Welding (pp. 82-92). National Institute of Standards and Technology.