Study on the Forecasting of the Hot Corrosion Resistance of Typical Superalloys for Aeroengines

The high-temperature components of aeroengines are in contact with gas flow for a long time, making them susceptible to hot corrosion, which can affect the reliability and lifespan of aeroengines. In this study, five types of superalloys commonly used in the high-temperature components of Aeroengines are selected for gas-based hot corrosion tests, and the corrosion rates are calculated using the weight loss method. Gradient Boosting Regression Tree (GBRT) machine learning algorithm is utilized to establish a corrosion rate forecasting model. The evaluation results show the predictability of this method. The effect of input parameters, including main alloy chemical composition and corrosion time, on the corrosion rate was discussed using GBRT and critical factors are obtained. These results provide a reference for the protection of aeroengines from hot corrosion.