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

S. Simanjuntak, M.K. Cavanaugh, D.S. Gandel, M.A. Easton, M.A. Gibson, N. Birbilis

Research output: Contribution to journalArticleResearchpeer-review

18 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)199 - 208
Number of pages10
JournalCorrosion
Volume71
Issue number2
DOIs
Publication statusPublished - 2015

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