Both commercial and model Ni-based alloys were tested in 1-h cycles at 800-950°C in wet air, and the oxide scales formed on wrought Ni-(14-25)wt%Cr binary alloys were characterized.
Jiheon Jun / Dongwon Shin / Sebastien Dryepondt, / J. Allen Haynes / Bruce A. Pint
For automotive exhaust valve applications, future vehicles will need affordable, durable materials capable of operating at higher temperatures with predictable response to severe oxidizing environments. Both commercial and model Ni-based alloys were tested in 1-h cycles at 800-950°C in wet air, and the oxide scales formed on wrought Ni-(14-25)wt%Cr binary alloys were characterized to gain a better understanding of the behavior of chromia-forming alloys under these conditions. The mass change curves were used to quantify the behavior of the tested alloys and fit growth and spallation rates using the kp-p model. We systematically analyzed the correlation between elemental alloy compositions and the manually fitted kp and p values to select high-ranking features to be included in a machine learning analysis. The machine learning models for the rate, kp, could be trained with a surprisingly high accuracy even with limited data, while only modest fitting was obtained for p, the spallation parameter. A preliminary theoretical framework that can predict kp and p of hypothetical alloys was established, however, improving the accuracy of surrogate models is needed to assist in alloy development for this transportation application.
Key words: Ni alloy, chromia scales, cyclic oxidation, exhaust valve, mass change modeling, correlation analysis, machine learning