What is it about?

Thermal Barrier Coatings (TBC) are ceramic coatings applied on metallic surfaces for protecting from high temperatures. The performance of the components is based on the health of the TBCs. So assessment of the TBC layer thickness and thermal properties are critical without disturbing the structural integration. A huge amount of data is required to make an accurate prediction of parameters using deep neural networks. Data is synthetically generated using numerical simulations and fed into the AI model along with real-world non-destructive testing data. The authors of this study demonstrate the development of a novel technique for the prediction of thermal conductivity, heat capacity, and thickness measurement using artificial intelligence. Based on the prediction error measures, the proposed study exhibits accurate and timely prediction of thermal properties and thickness measurement which aids in the life extension and better performance of Thermal Barrier Coated components used in aircraft.

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Why is it important?

Our methodology demonstrates an efficient and fast prediction of TBC parameters by combining AI and non-destructive testing techniques.

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This page is a summary of: Simulation-assisted AI for the evaluation of thermal barrier coatings using pulsed infrared thermography, Journal of Applied Physics, August 2022, American Institute of Physics, DOI: 10.1063/5.0088304.
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