Prediction of residual stresses in gas arc welding by back propagation neural network

This paper presents the development of a back propagation neural network model for the prediction of maximum residual stresses produced in gas metal arc welding process. The model is based on results obtained from finite element models. The thickness of the plate, electrode size, welding speed, current/voltage intensity have been considered as the input parameters and the maximum residual stresses due to welding as output parameters in the development of the model. The Levenberg–Marquardt method as a feed forward back propagation method was used in this investigation. The neural network predictions were then compared with the finite element results for accuracy, and the comparison showed that the results obtained from neural network model were sufficiently accurate in predicting the residual stresses.