SUMMARY
Ni-base superalloys have long been the material of choice in hot-section of the gas turbine engines and particularly turbine blades owing to their superior high-temperature performance. However, under the influence of the severe operating environment that imposes complex loading and thermal cycles to the components, the material undergoes various structural evolution and degradation that deteriorates the properties of interest and performance. Identifying and characterizing the relationship between the actual operating exposure conditions and the resulting state of the microstructure is of paramount importance to the component remaining lifetime prediction efforts. In this transdisciplinary research, the outlined critical need is addressed by proposing a framework to develop data-driven models that offer the capability to estimate the extent of the structural evolution from a given service condition. In addition, the introduced protocol is effective to tackle the backward problem by which the prior service history of a known microstructure state can be determined. The proposed research involves performing thermomechanical fatigue experiments to simulate service environment, employing electron microscopy characterization techniques, and adopting statistical and data analytics algorithms to mine the large acquired microstructure database.