  Although of practical importance, there is no established modeling framework
to accurately predict high-temperature cyclic oxidation kinetics of
multi-component alloys due to the inherent complexity. We present a data
analytics approach to predict the oxidation rate constant of NiCr-based alloys
as a function of composition and temperature with a highly consistent and
well-curated experimental dataset. Two characteristic oxidation models, i.e., a
simple parabolic law and a statistical cyclic-oxidation model, have been chosen
to numerically represent the high-temperature oxidation kinetics of commercial
and model NiCr-based alloys. We have successfully trained machine learning (ML)
models using highly ranked key input features identified by correlation
analysis to accurately predict experimental parabolic rate constants (kp). This
study demonstrates the potential of ML approaches to predict oxidation kinetics
of alloys over a wide composition and temperature ranges. This approach can
also serve as a basis for introducing more physically meaningful ML input
features to predict the comprehensive cyclic oxidation behavior of
multi-component high-temperature alloys with proper constraints based on the
known underlying mechanisms.
