The influence of iron, manganese, and zirconium on the corrosion of magnesium: An artificial neural network approach

A total of 71 custom alloys were prepared and tested in order to produce a statistically relevant spread of compositions containing a range of iron (Fe), manganese (Mn), and zirconium (Zr) additions to magnesium (Mg). Alloys were produced using Mg-Fe/Zr/Mn master alloys and were tested using potentiodynamic polarization and mass loss (immersion) testing to ascertain the relative rates of corrosion. The rationale was to empirically explore the concept of threshold or tolerance limits, namely any variation in tolerance limits depending on the relative Fe, Mn, and Zr content, with direct relevance to aluminum (Al) free Mg-alloys. Data was analyzed using an artificial neural network (ANN) model. It was shown that Mn has a moderating effect on Fe with regard to the acceleration of the corrosion rate, even in the simple Mg-Fe-Mn system and in the absence of Al.