Predicting Atmospheric Corrosion of Aluminum 2024 Coupons in Coastal Environments with Data Analytics

Abstract

How can we design short-term laboratory corrosion tests to adequately simulate long-term outdoor atmospheric corrosion? To achieve this objective, models need to be devised to rank and quantify critical environmental variables that influence atmospheric corrosion. This effort provides a data-driven analysis of environmental factors that can be used to quantitatively model atmospheric corrosion aluminum alloy 2024-T3 (AA2024-T3) samples placed at three unique coastal sites in the state of Florida, USA, for various time periods over an 18-month total period of exposure. Atmospheric corrosion proceeds via several processes that take place in sequence and/or in parallel across multiple classes of matter: the atmosphere, condensed aqueous solution, organic coatings, oxide scales, precipitated salts, and microstructurally heterogeneous metal alloys. Several 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 modeling approach that pairs data analytics with scientific insight is required. To support this objective, data was collected for AA2024-T3 coupons based on exposure over an 18-month period at three coastal sites in Florida, USA. Methods to summarize the environmental exposure metrics for each time period were developed using standard statistical metrics (mean, standard deviation) as well as a more complete set of metrics, known as the Catch-22 algorithms, developed by The University of Oxford. The corrosion coupon data was aggregated into a data framework that included the metrics of mass loss per unit area, linear corrosion rate, and a parabolic corrosion constant, as well as generation of additional data by sample differencing that considers cumulative corrosion occurring between time periods. An automated approach was then developed that queries public and/or pay-to-access websites for environmental data to build an exposure profile for a sample placed at a known location (specified by latitude/longitude) and over a given range of dates. We evaluated 288 total variables in the exposure profile. Of these 288 variables, five key variables were determined to have a quantitative effect on the corrosion rate and mass loss per unit area, and these include mean precipitation, the range of temperatures, the minimum wind speed, the standard deviation of ozone exposure, and the maximum solar irradiance. This work serves to help with the selection of appropriate variables to be used in designs for a laboratory-simulation exposure chamber that would mimic service environment and accelerate the development of advanced materials degradation test protocols. The approach developed herein can be applied to other materials of interest, different locations, and adapted to other metrics of corrosion such as localized corrosion depth and volume, due to pitting.