High temperature oxidation of corrosion resistant alloys from machine learning

Parabolic rate constants, k(p), were collected from published reports and calculated from corrosion product data (sample mass gain or corrosion product thickness) and tabulated for 75 alloys exposed to temperatures between similar to 800 and 2000 K (similar to 500-1700 C-o; 900-3000 F-o). Data were collected for environments including lab air, ambient and supercritical carbon dioxide, supercritical water, and steam. Materials studied include low- and high-Cr ferritic and austenitic steels, nickel superalloys, and aluminide materials. A combination of Arrhenius analysis, simple linear regression, supervised and unsupervised machine learning methods were used to investigate the relations between composition and oxidation kinetics. The supervised machine learning techniques produced the lowest mean standard errors. The most significant elements controlling oxidation kinetics were Ni, Cr, Al, and Fe, with Mo and Co composition also found to be significant features. The activation energies produced from the machine learning analysis were in the correct distributions for the diffusion constants for the oxide scales expected to dominate in each class.

Automated summary

The study investigated the relationships between oxidation kinetics and composition by collecting and calculating parabolic rate constants for 75 alloys exposed to high temperatures. Various analytical methods were used, with supervised machine learning techniques showing the lowest standard errors. Nickel, chromium, aluminum, and iron were identified as the most significant elements controlling oxidation kinetics in the study.