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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14 August 2022
Research Article|
August 09 2022
Simulation-assisted AI for the evaluation of thermal barrier coatings using pulsed infrared thermography
Special Collection:
Non-Invasive and Non-Destructive Methods and Applications Part II
Sruthi Krishna K P
;
Sruthi Krishna K P
a)
(Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft)
1
Center for Non-Destructive Evaluation, Indian Institute of Technology Madras
, Chennai 600036, India
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Nithin Puthiyaveettil
;
Nithin Puthiyaveettil
(Conceptualization, Investigation, Methodology, Validation, Visualization, Writing – review and editing)
1
Center for Non-Destructive Evaluation, Indian Institute of Technology Madras
, Chennai 600036, India
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Srinivasa Chakravarthy V
;
Srinivasa Chakravarthy V
(Conceptualization, Project administration, Resources, Supervision, Writing – review and editing)
2
Computational Neuroscience Laboratory, Indian Institute of Technology Madras
, Chennai 600036, India
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Krishnan Balasubramaniam
Krishnan Balasubramaniam
a)
(Conceptualization, Project administration, Resources, Supervision, Writing – review and editing)
1
Center for Non-Destructive Evaluation, Indian Institute of Technology Madras
, Chennai 600036, India
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Note: This paper is part of the Special Topic on Non-Invasive and Non-Destructive Methods and Applications Part II.
J. Appl. Phys. 132, 064902 (2022)
Article history
Received:
February 15 2022
Accepted:
July 11 2022
Citation
Sruthi Krishna K P, Nithin Puthiyaveettil, Srinivasa Chakravarthy V, Krishnan Balasubramaniam; Simulation-assisted AI for the evaluation of thermal barrier coatings using pulsed infrared thermography. J. Appl. Phys. 14 August 2022; 132 (6): 064902. https://doi.org/10.1063/5.0088304
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