Machine Learning-Assisted Prediction of Corrosion Behavior of 7XXX Aluminum Alloys

Abstract

High-strength and lightweight 7XXX Al alloys are widely applied in aerospace industries. Stress corrosion cracking (SCC) in these alloys has been extensively discussed, and electrochemical corrosion should be brought to the forefront when these materials are used in marine atmospheric environments. This work obtained the corrosion potentials (Ecorr) and corrosion rates of 40 as-cast 7XXX Al alloys by potentiodynamic polarization tests and immersion tests, respectively; then, chemical compositions and physical features were used to build a machine learning model to predict these parameters. RFR was used for the prediction model of Ecorr with the features Cu, Ti, Al, and Zn, and GPR for that of the corrosion rate with the features of specific heat, latent heat of fusion, and proportion of p electrons. The physical meaning and reasonability were discussed based on the analysis of corrosion morphology and precipitated composition. This work provides a reference for the design of corrosion-resistant 7XXX Al alloys and shows a method of conducting corrosion mechanism evaluation by using machine learning.