Abstract
This study focuses on the design and evaluation of corrosion-resistant aluminum alloys using galvanostatic test and quadratic regression analysis. Al-Zr and Al-Mn-Zr alloys were evaluated through both galvanostatic and immersion tests. The galvanostatic test effectively simulated localized corrosion mechanisms, reducing testing duration from hundreds or thousands of hours to just tens of hours. Scanning Kelvin Probe Force Microscopy (SKPFM) and SEM/EDS analyses revealed that alloys with higher potential differences between precipitates and the Al matrix showed increased susceptibility to localized corrosion. A quadratic regression model was developed to describe the relationship between maximum pit depth and the contents of Zr and Mn, achieving a high coefficient of determination (R² = 0.975). The model identified an optimal alloy composition of 0.16 wt.% Zr and 0.41 wt.% Mn, resulting in a minimized pit depth of 24.40 μm. This research demonstrates that combining galvanostatic test with regression analysis shorten the required time for development of corrosion-resistant aluminum alloys, providing a framework for optimizing compositions and predicting corrosion resistance, thereby significantly reducing design time.
