High temperature alloys span multiple classes of materials including low alloy steels, stainless steels, nickel-chromium alloys, superalloys, aluminides, and, of more recent interest high entropy alloys, among others. Whereas many high temperature alloys deviate from the parabolic oxide growth law, the parabolic rate constant kp remains a useful indicator of the oxidation susceptibility for a given material. To design new classes of materials, and help with materials selection, it would be useful to directly predict the oxidation rate constants from materials features, such as composition and microstructure. With this goal in mind, parabolic rate constants have been collected from the literature for 75 alloys exposed to temperatures between 900 and 3000°F. Environments incorporated into the analysis include lab air, ambient and supercritical carbon dioxide, supercritical water, and steam. Predictive models for the oxidation rate constant were developed using machine learning and analyzed to provide insights into the leading factors producing corrosion resistance in these materials.

Subject
Artificial neural networks, Materials, Composition, Stainless alloys, Materials performance, Nickel based alloys, Clusters, Activation energy, Constants, Superalloys, Elements, Alloys, Oxidation
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2023
Association for Materials Protection and Performance (AMPP)
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