Neural Networks Applied to Welding. Two Examples.

Abstract

Neural network analysis provides for a powerful means for non-linear regression analysis that can be applied to a wide variety of problems. This paper describes the application of neural networks to two welding areas. The general approach for the development of the back propagation neural networks is described, including the method that was used to identify the optimum neural network architecture and the best neural network. The first application considers the weld profile shape in pulsed laser aluminum welds and the prediction of the profiles as a function of weld process conditions. It is shown that the neural network predictions are reasonably accurate in most cases, in spite of the fact that the training data set was quite small. The second application considers the prediction of Ferrite Number in stainless steel welds as a function of weld composition. For this application an extensive training data set was available. The neural network that was developed was compared to conventional means for predicting Ferrite Number and it was shown that the neural network was considerably more accurate than other currently available methods.