Predicting the Parabolic Rate Constants of High-Temperature Oxidation of Ti Alloys Using Machine Learning

In this study, we attempt to build a statistical (machine) learning model to predict the parabolic rate constant $$(k_{\text{P}} )$$ for the high-temperature oxidation of Ti alloys. Exploring the experimental studies on high-temperature oxidation of Ti alloys, we built our dataset for machine learning. Apart from the alloy composition, we included the constituent phase of the alloy, temperature of oxidation, time for oxidation, oxygen and moisture content, remaining atmosphere (gas except O2 gas in dry atmosphere), and mode of oxidation testing as the independent features while the parabolic rate constant $$(k_{\text{P}} )$$ is set as the target feature. We employed three different ML models to predict the ‘$$k_{\text{P}}$$’ for Ti alloys. Among the regression models, the gradient boosting regressor yields the coefficient of determination (R2) of 0.92 for $$k_{\text{P}}$$. The knowledge gained from this study can be used to design novel Ti alloys with excellent resistance towards high-temperature oxidation.