Assessment of the effect of existing corrosion on the tensile behaviour of magnesium alloy AZ31 using neural networks

A concept has been devised to assess the effect of existing corrosion damage on the residual tensile properties of structural alloys and applied for the magnesium alloy AZ31. The concept based on the use of a radial basis function neural network. An extensive experimental investigation, including metallographic corrosion characterization and mechanical testing of pre-corroded AZ31 magnesium alloy specimens, was carried out to derive the necessary data for the training and the prediction module of the developed neural network model. The proposed concept was exploited to successfully predict: the gradual tensile property degradation of the alloy AZ31 to the results of gradually increasing corrosion damage with increasing corrosion exposure.