Atmospheric corrosion of aluminum alloy AA2024-T3 samples was monitored at three different sites in Florida: NRL-Key West, Kennedy Space Center and Daytona Beach/Battelle site. The samples were exposed over different periods of exposure and for different time periods. A systematic approach was used to correlate exposure conditions with the amount of corrosion as measured by mass loss. An analytical approach was developed that involved collecting meteorological data for the three sites from multiple sources, performing data quality checks, and analyzing the trends in both the corrosion data and the meteorological data. Machine learning through feature selection and regression approaches was used to identify leading meteorological factors that quantitatively control extent of corrosion. Key features determined to have a quantitative effect on corrosion rate and mass loss per unit area were collected and include mean precipitation, the range of temperatures, the minimum wind speed, the standard deviation of ozone exposure, and the maximum solar irradiance. This approach could be applied to other materials of interest, different locations, and adapted to other corrosion metrics such as localized corrosion depth and pit volumes.
Atmospheric corrosion proceeds via several processes that proceed in sequence and/or parallel across multiple classes of matter (the atmosphere, condensed aqueous solution, polymer coatings, oxide scales, precipitated salts, and microstructurally heterogeneous metal alloys). Multiple physical and chemical phenomena contribute to the process of corrosion, including mass-transport, electrochemical effects, metal dissolution, grain-boundary transport, etc. For this reason, it is difficult to directly predict, using fundamental physics or chemical principles, the corrosion rate of a metal in its environment. Likewise, it is difficult to directly extrapolate the results of short-term tests to long-term tests solely from physical principles. A data-driven modeling approach can assist in identifying the key environmental factors driving atmospheric corrosion. This paper provides a data-driven analysis of environmental factors that influence atmospheric corrosion of a series of aluminum alloy 2024-T3 (AA2024-T3) samples placed at three unique coastal sites in the state of Florida, USA, for different time periods.
