TY - GEN
T1 - Cyclic oxidation behavior of Ni-Cr alloys in wet air
AU - Jun, Jiheon
AU - Shin, Dongwon
AU - Dryepondt, Sebastien
AU - Haynes, J. Allen
AU - Pint, Bruce A.
N1 - Publisher Copyright:
© 2018 by NACE International.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Chromia scales
KW - Correlation analysis
KW - Cyclic oxidation
KW - Exhaust valve
KW - Machine learning
KW - Mass change modeling
KW - Ni alloy
UR - http://www.scopus.com/inward/record.url?scp=85053490054&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85053490054
SN - 9781510864405
T3 - NACE - International Corrosion Conference Series
BT - Corrosion Conference and Expo 2018
PB - National Assoc. of Corrosion Engineers International
T2 - Corrosion Conference and Expo 2018
Y2 - 15 April 2018 through 19 April 2018
ER -