Quantitative Evaluation of the Ability of Accelerated Tests to Simulate On-Road Corrosion Morphology of Automotive Aluminum Alloys

Abstract

Three wrought automotive Al-Mg-Si alloys (6xxx-series) were exposed to an on-road service environment and in six standard laboratory-accelerated exposure tests. Optical micrographs of all these exposures were obtained and the resulting corrosion morphology was quantified using fractal dimension analysis and corrosion boundary length-to-area ratios. Additionally, the images were also used to train a convolutional neural network (CNN)-based pattern recognition algorithm, which was then used to quantitatively identify which accelerated test was the closest match to the field exposures. Overall, no single accelerated test could fully capture the on-road results or ubiquitously be the most appropriate test regardless of alloy and temper. However, results from fractal dimension and length-to-area ratio analyses identified that among the tests studied, those with acidified electrolytes are more appropriate for matching on-road corrosion morphology. The CNN algorithm output also agreed with this finding, indicating that the results from tests with acidified electrolytes correlated to field morphology with a confidence >70% for most of the images tested, thus showing the utility of these methods in providing quantitative bases for morphology comparison. Assessed in the context of literature evidence for localized corrosion mechanisms in 6xxx-series alloys, these results also indicated that pH may play an influential role in how corrosion morphology develops in these alloys upon exposure to a complex on-road environment.