## 4 Conclusions  
This study introduces the design and preparation of an atmospheric plasma-spraying process for EMAP TBCs. Ten sets of different types of spraying process data were set up to prepare EMAP TBCs and conventional TBC specimens, and the effects of the spraying process on the microstructure and thermal cycle life of EMAP TBCs were investigated. Three machine learning models were developed to evaluate the nonlinear relationship between the spraying process data and the MAP content and coatings' thermal cycle life. 1. Sintering and thermal cycling experiments were carried out on conventional TBCs and EMAP TBCs. The results showed that the sintering resistance of EMAP TBCs was better than that of conventional TBCs by 500%, and the thermal cycle life was higher than that of conventional TBCs by 200%. 2. The effects of spraying process data on the microstructure and lifetime of EMAP TBCs were systematically investigated. The results showed that the lifetime of EMAP TBCs was shorter when the powder feeder 1 and powder feeder 2 distance was 30 mm. The best thermal cycle life of the coatings was achieved when the powder feeder distance was 35 mm, the powder feeder 1 rate was 12 g/min, and the second-phase powder was MAP. 3. By establishing machine learning models, the relationship between spraying parameters among agglomerated micron particle content and service life was evaluated. The results showed that among the three machine learning models, the R\({}^{2}\) of GA-SVM was as high as 0.951 and 0.979, respectively, and the evaluation error was the smallest, which was the optimal model. In this study, the effects of spraying process parameters on the microstructure and service life of EMAP TBCs were systematically analyzed. It accurately evaluates the nonlinear relationship between the spraying process parameters and the microstructure data and thermal cycle life of EMAP TBCs through the machine learning prediction model, which provides a reference for further improving the service life of TBCs in engineering applications. However, in this study, there are few data sets for machine learning, and there are some limitations. In future work, we will continue to expand the number of data sets, thereby improving the accuracy of machine learning prediction. The effects of other spraying process parameters (such as spraying power, powder feeding rate of the second powder feeder, etc.) on the thermal cycle life of the coating will be further studied. \begin{table}
\begin{tabular}{c c c c} \hline
**Forecasting Model** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline BP & 0.881 & 1.583 & 1.694 \\ SVM & 0.922 & 1.127 & 1.003 \\ GA-SVM & 0.951 & 0.722 & 0.534 \\ \hline \end{tabular}
\end{table} 
Table 3: MAP content prediction error analysis. **Author Contributions:** Conceptualization, C.Y., Z.Z., J.H. and D.Y. ; data curation, Y.W., F.W., Z.Z., J.H. and D.Y. ; formal analysis, C.Y., F.W., H.L. and D.Y. ; funding acquisition, D.Y. ; investigation, S.X. and D.Y. ; methodology, S.X., Z.X., C.Y., J.H., D.Y. and G.Y. ; project administration, H.L. and D.Y. ; resources, Z.X., H.L. and D.Y. ; software, S.X., Z.X., Z.Z. and D.Y. ; supervision, Z.X., C.Y., Y.W., H.L. and D.Y. ; validation, S.X., Y.W., D.Y. and G.Y. ; visualization, Y.W. ; writing--original draft, S.X. ; writing--review and editing, Z.X., H.L. and D.Y. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the National Natural Science Foundation of China (No. 52205547, D.Y. ), the Excellent Young Talents Fund of Higher Education Institutions of Anhui Province (No. 2024 AH030006, D.Y. ), the Key Research and Development Projects in Anhui Province (2022a05020004, D.Y. ), the Open Fund Project of State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines (No. SKLMRDPC22 KF22, D.Y. ), the Undergraduate Teaching Quality Improvement Program Project of Anhui Polytechnic University (No. 2023 jyxm76, D.Y., No. 2023 szyzk39, D.Y., No. 2024 szyzk51, D.Y.