文献类型：期刊文献

英文题名：Prediction of Thermal Barrier Coatings Microstructural Features Based on Support Vector Machine Optimized by Cuckoo Search Algorithm

作者：Ye, Dongdong[1];Wang, Weize[1];Xu, Zhou[1];Yin, Changdong[1];Zhou, Haiting[2];Li, Yuanjun[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Key Lab Safety Sci Pressurized Syst, Minist Educ, Shanghai 200237, Peoples R China;[2]China Jiliang Univ, Dept Qual & Safety Engn, Hangzhou 310018, Peoples R China

年份：2020

卷号：10

期号：7

外文期刊名：COATINGS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000557539700001)】；

基金：This research was funded by the National Natural Science Foundation of China (No. 51775189) and Science and Technology Commission of Shanghai Municipality Project (No. 16DZ2260604).

语种：英文

外文关键词：microstructural feature; thermal barrier coatings (TBCs); support vector machine; cuckoo search algorithm

摘要：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 (R-2) 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.