Data-driven AI for the automated classification of the isothermal heat-treated thermal barrier coatings using pulsed infrared thermography

Abstract

Development of reliable age prediction models are crucial in monitoring the formation of oxide layer and degradation of TBC at regular intervals. This study proposes an automated classification of isothermal heat-treated TBC samples using temperature data, which helps in predicting the TBC life and monitoring the TBC degradation. TBC-coated samples are isothermal heat-treated at 1000 °C, and the initial growth of thermally grown oxide is monitored using a non-destructive thermal imaging technique. The proposed study integrates data-driven AI (DAI) models and feature extraction techniques to interpret complex thermal patterns measured from the TBC coating surface. The performance of the proposed classification framework is tested using deep learning and classical machine learning models with different types and window sizes of input data. Input data used for validation are raw experiment data, logarithmic of experiment data, polynomial fit data, and thermal signal reconstruction fit coefficients. The maximum classification performance is obtained with gated recurrent unit with accuracy and F1-score of 89.2% and 89.0%, respectively with raw temperature data as input of window 300. The study demonstrates that the proposed DAI approach effectively predicts the age of thermal barrier coatings under isothermal heat-treatment conditions by correlating the thermal response with coating degradation.