Prediction of Microstructure and Mechanical Properties of Atmospheric Plasma-Sprayed 8YSZ Thermal Barrier Coatings Using Hybrid Machine Learning Approaches

The preparation of thermal barrier coatings (TBCs) is a complex process involving the integration of physics and chemistry, mainly involving the flight behavior and deposition behavior of molten particles. The service life and performance of the TBCs were determined by various factors, especially the preparation process parameters. In this work, to set up the quantitative characterization model between the preparation process parameters and the performance characteristic parameters, the ceramic powder particle size, spraying power and spraying distance were treated as the model input parameters, the characteristic parameters of microstructure properties represented by the porosity, circularity and Feret's diameter and the mechanical property represented by the interfacial binding strength and macrohardness were treated as the model output. The typical back propagation (BP) model and extreme learning machine (ELM) model combined with flower pollination algorithm (FPA) optimization algorithm were employed for modeling analysis. To ensure the robustness of the obtained regression prediction model, the k-fold cross-validation method was employed to evaluate and analyze the regression prediction models. The results showed that the regression coefficient R value of the proposed FPA-ELM hybrid machine learning model was more than 0.94, the root-mean-square error (RMSE) was lower than 2 and showed better prediction accuracy and robustness. Finally, this work provided a novel method to optimize the TBCs preparation process, and was expected to improve the efficiency of TBCs preparation and characterization in the future.