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 growth transition of different Fe–Mn alloys in various oxygen partial pressures (10–30 kPa) and gas flow rates (26.6 and 53.3 sccm) in a temperature range of 950–1150 °C. Oxidation kinetics was investigated using a thermogravimetric analyzer (TGA) under controlled atmosphere. Linear oxide growth was observed within the first 20 minutes of oxidation. The linear rate constant was significantly increased by increasing the oxygen partial pressure or the flow rate of the oxidizing gas. The morphology of the oxide layer was determined by scanning electron microscopy (SEM). The crystal structure of the oxides formed was followed by in-situ X-ray diffraction (XRD), confirming that the growing layer consists of wustite mainly, which upon slow cooling to room temperature, transformed into magnetite. Energy-dispersive X-ray spectroscopy (EDS) showed that the atomic ratio of Fe+Mn to O was ~ 1.03:1 in the oxide scale, corresponding to Fe(Mn)O formation. Based on the characterization and a model for linear growth kinetics, it is concluded that the oxidation rate is controlled by the diffusion of oxidizing molecules through the gas layer to the sample’s surface. The findings led to a better understanding of initial oxidation behavior and provided a pathway for improved insight into the high-temperature oxidation behavior for more complex alloys.@en

The parabolic growth rate constant (kp) 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 (composition and temperature) and the target value (kp). The models are evaluated by the indices: Mean Absolute Error, Mean Squared Error, Root Mean Squared Error and Coefficient of Determination. The steel composition regarding Cr and Ni content and the temperature were the most significant input features controlling the oxidation kinetics.@en

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 formation of oxide phases and the composition profile of the steel alloy’s constituents in a short time due to external oxidation. The model is applied to high-temperature oxidation of Fe–Mn alloys under different conditions. Oxidizing experiments executed with a thermogravimetric analyzer (TGA) on Fe–Mn alloys with different Mn contents (below 10 wt %) are used to determine kinetic parameters that serve as an input for the model. The mass gain data as a function of time show both linear and parabolic regimes. The results of the numerical simulations are presented. The effect of different parameters, such as temperature, Mn content of the alloy, oxygen partial pressure, and oxidizing gas flow rate on the alloy composition and oxide phases formed, is determined. It is shown that increasing the temperature and decreasing the oxygen partial pressure both lead to a thicker depleted area.@en

H igh-temperature (HT) oxidation plays a significant role in various stages of the steelmaking process, including hot rolling. When exposed to high temperatures and oxygen partial pressure, the steel composition near the surface can be altered as alloying elements deplete. Additionally, the characteristics of the oxide scale, such as thickness and phase composition, vary depending on the oxidation conditions. Due to the experimental challenges of studying such rapid processes under extreme conditions, predictive models are necessary to estimate the substrate surface and oxide scale composition as well as the general oxidation rate of the alloy....@en