Artificial neural networks applied to arc welding process modeling and control

Artificial neural networks have been studied to determine their applicability to modeling and control of physical processes. Some basic concepts relating to neural networks and how they can be used to model weld-bead geometry in terms of the equipment parameters selected to produce the weld are explained. Approaches to utilizing neural networks in process control are discussed. The need for modeling transient as well as static characteristics of physical systems for closed-loop control is pointed out, and an approach to achieving this is presented. The performance of neural networks for modeling is evaluated using actual welding data. It is concluded that the accuracy of neural network modeling is fully comparable with the accuracy achieved by more traditional modeling schemes