The development of predictive models for the accurate estimation of thermo-physical properties of the Thermal Barrier Coated (TBC) aero-engine components is critical in assessing component life and maintenance. TBCs are multi-layer systems applied on metallic structures operating at higher temperatures, such as aero-engine parts and gas turbine blades. These thermally insulating materials prolong the component life by limiting the thermal exposure of structural components. In this study, simulation-assisted Artificial Intelligence (AI) is developed to predict thermal conductivity (k), heat capacity (ρCp), and thickness measurement of TBC from thermal responses of samples with varying topcoat layer thicknesses. The dataset used in the AI model is a low-fidelity thermal profile from a multi-layer heat transfer model of the TBC system for training the neural network and high-fidelity thermogram from pulsed thermography experiments that are used for validation of the trained neural network. The proposed method demonstrated potential in the prediction of thermo-physical properties for real samples with a newly coated topcoat layer of thickness measurement varying from 24 to 120 μm, with a mean absolute percentage error (MAPE) for k and ρCp predictions of 1.71% and 1.37%, respectively, and for thickness prediction, MAPE ranges from 0.81% to 6.14%. This work explores the possibilities of merging a large set of low-fidelity simulation data and a small set of high-fidelity experimental data to train the deep neural network to achieve promising results in real-world thermography experiments.
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