Microstructural features have a vital effect on the comprehensive performance of thermal barrier coatings (TBCs) and highly depend on the thermal spray processing parameters. Herein, a novel hybrid machine-learning method was proposed to predict the microstructural features of TBCs using thermal spray processing parameters based on a support vector machine method optimized by the cuckoo search algorithm (CS-SVM). In this work, atmospheric-plasma-sprayed (APS) TBCs samples with multifarious microstructural features were acquired by modifying the spray powder size, spray distance, and spray power during thermal spray processing. The processing parameters were used as the inputs for the CS-SVM model. Then, the porosity, the pore-to-crack ratio, the maximum Feret’s diameter, the aspect ratio, and the circularity were counted and treated as the targets for the CS-SVM model. After optimization and training procedure of the CS-SVM model, the predicted results were compared to the results of experimental data, as a result, the squared correlation coefficient (R²) of CS-SVM model showed that the prediction accuracy reached by over 95%, and the root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were less than 0.1, which also verified the reliability of the CS-SVM model. Finally, this study proposed a novel and efficient microstructural feature prediction that could be potentially employed to improve the performance of TBCs in service.

中文翻译：

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基于布谷鸟搜索算法优化的支持向量机的热障涂层微结构特征预测

微观结构特征对热障涂层（TBC）的综合性能具有至关重要的影响，并且高度依赖于热喷涂工艺参数。在此，提出了一种新的混合机器学习方法，该方法基于布谷鸟搜索算法（CS-SVM）优化的支持向量机方法，利用热喷涂工艺参数预测TBC的微结构特征。在这项工作中，通过在热喷涂过程中修改喷涂粉末的尺寸，喷涂距离和喷涂功率，获得了具有多种微结构特征的大气等离子体喷涂（APS）TBCs样品。处理参数用作CS-SVM模型的输入。然后，孔隙率，孔隙与裂纹的比率，最大费雷特直径，长径比，计算圆度并将其作为CS-SVM模型的目标。经过CS-SVM模型的优化和训练过程，将预测结果与实验数据的结果进行了比较，得出平方相关系数（CS-SVM模型的R ²）表明预测精度达到95％以上，并且均方根误差（RMSE），平均绝对误差（MAE）和平均绝对百分比误差（MAPE）均小于0.1，这还验证了CS-SVM模型的可靠性。最后，这项研究提出了一种新颖而有效的微结构特征预测，可潜在地用于提高服务中TBC的性能。