Prediction of flow curves and forming limit curves of Mg alloy thin sheets using ANN-based models

Multivariable empirical models based on artificial neural networks were developed in order to predict the flow curves and forming limit curves of AZ31 magnesium alloy thin sheets, in warm forming conditions, versus process parameters and fibre orientation. Experimental tensile and hemispherical punch tests were carried out in order to obtain the experimental data set, in terms of flow curves and forming limit curves, to be used to train the artificial neural networks. A preliminary study, based on the leave one-out-cross validation methodology, has proven the very good predictive capability of the ANN-based models in modelling both flow curves (flow stress level, curve shape and strain at the onset of necking) and forming limit curves (curve shape, major strain values and minor strain limit) under different process conditions and fibre orientations. Then, the generalisation capability of the neural models in capturing the effect of process parameters and fibre orientation on flow curves and formability has been proven by the excellent agreement, in terms of the high correlation coefficients, low relative errors and average absolute relative errors, between predicted and experimental results not investigated in the training set.