Predicting the parabolic growth rate constant for high-temperature oxidation of steels using machine learning models

The parabolic growth rate constant (k_p) of high-temperature oxidation of steels is predicted via a data analytics approach. Four machine learning models including Artificial Neural Networks, Random Forest, k-Nearest Neighbors, and Support Vector Regression are trained to establish the relations between the input features ...

Numerical Model For Short-Time High-Temperature Isothermal Oxidation of Fe–Mn Binaries at High Oxygen Partial Pressure

Since the oxidation reactions in the process of steel production occur in harsh conditions (i.e., high temperatures and gas atmospheres), it is practically impossible to observe in situ the compositional changes in the steel and the formed oxide scale. Hence, a coupled thermodynamic-kinetic numerical model is developed that predicts the...

Initial High-Temperature Oxidation Behavior of Fe–Mn Binaries in Air: The Kinetics and Mechanism of Oxidation

High-temperature oxidation of steels can be relatively fast when exposed to air. Consequently, elucidating the effect of different parameters on the oxidation mechanism and kinetics is challenging. In this study, short-time oxidation was investigated to determine the oxidation mechanism, the affecting parameters, and the linear-to-parabolic...