A Prediction Method for Local Creep Strain of Directionally Solidified Superalloys and Turbine Blades

Evaluating the service conditions and the corresponding strain at high-pressure turbine bladesTurbine blades is crucial for the safe service and maintenance of aircraft engines. However, due to the harsh environment in turbines and the complex geometry of blades, it is difficult to directly monitor the variation of their service temperatures, stresses, and strains. In this work, an approach to predicting the equivalent service conditions and the local strainLocal strain of directionally solidified superalloysDirectionally solidified superalloys and turbine bladesTurbine blades was developed by integrating high-throughput creepCreep tests and machine learning tools. A large amount of experimental data was obtained using the flat specimens with the continuously variable cross-section and digital image correlationDigital image correlations technique. Then, the quantitative relationship between temperature, stress, strain, time, and the essential microstructureMicrostructure parameters was established with the help of machine learningMachine learning models. The established machine learningMachine learning models were then employed to predict the service conditions and the corresponding strain of a directionally solidified superalloyDirectionally solidified superalloys and a turbine bladeTurbine blades. Finally, the applicability and limitations of this method were discussed. The development of this method provides guidance for the service evaluationService evaluation of turbine bladesTurbine blades.