Artificial neural network models for predicting the corrosion behavior of friction stir processed AA5083

Abstract

Abstract Aluminum alloy 5083 has good corrosion resistance in marine environments and consists of a primary phase of α-Al and a secondary phase of β-Mg2Al3. AA5083 was subjected to friction stir processing (FSP. AA5083 was friction stir processed by varying the tool rotation speed, tool traverse speed, and tool shoulder diameters as per face-centered central composite design. The specimens were subjected to a potentiodynamic polarization test in an artificial seawater solution to estimate the corrosion potential and corrosion rate of the alloy at three different temperatures. In this work, a feed-forward backpropagation network with the Levenberg–Marquardt training algorithm was developed to predict the corrosion potential and corrosion rate of the specimens as a function of the friction stir processing process parameters. The model predictions were in good agreement with the experimental results. The correlation coefficient of the models was approximately equal to unity, demonstrating the high prediction efficiency of the developed models.