Fatigue life estimation of non-penetrated butt weldments in ligth metals by artificial neural network approach

Abstract

This study presents a model for estimating the fatigue life of magnesium and aluminium non-penetrated butt-welded joints using Artificial Neural Network (ANN). The input parameters for the network are stress concentration factor Kt and nominal stress amplitude sa,n. The output parameter is the endurable number of load cycles N. Fatigue data were collected from the literature from three different sources. The experimental tests, on which the fatigue data are based, were carried out at the Fraunhofer Institute for Structural Durability and System Reliability (LBF), Darmstadt - Germany. The results determined with use of artificial neural network for welded magnesium and aluminium joints are displayed in the same scatter bands of SN-lines. It is observed that the trained results are in good agreement with the tested data and artificial neural network is applicable for estimating the SN-lines for non-penetrated welded magnesium and aluminium joints under cyclic loading. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.