Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength

Pengcheng Xu; Can Chen; Shuizhou Chen; Wencong Lu; Quan Qian; Yi Zeng

doi:10.1021/acsomega.2c01839

Machine Learning-Assisted Design of Yttria-Stabilized Zirconia Thermal Barrier Coatings with High Bonding Strength

ACS Omega. 2022 Jun 9;7(24):21052-21061. doi: 10.1021/acsomega.2c01839. eCollection 2022 Jun 21.

Authors

Pengcheng Xu¹, Can Chen², Shuizhou Chen³, Wencong Lu^{1

4}, Quan Qian³, Yi Zeng²

Affiliations

¹ Materials Genome Institute, Shanghai University, Shanghai 200444, China.
² The State Key Lab of High Performance Ceramics and Superfine Micro-structure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Shanghai 200050, China.
³ School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China.
⁴ Department of Chemistry, College of Sciences, Shanghai University, Shanghai 200444, China.

Abstract

As a high-quality thermal barrier coating material, yttria-stabilized zirconia (YSZ) can effectively reduce the temperature of the collective materials to be used on the surface of gas turbine hot-end components. The bonding strength between YSZ and the substrate is also one of the most important factors for the applications. Herein, the Gaussian mixture model (GMM) and support vector regression (SVR) were used to construct a machine learning model between YSZ coating bonding strength and atmospheric plasma spraying (APS) process parameters. First, GMM was used to expand the original 8 data points to 400 with the R value of leave-one-out cross-validation improved from 0.690 to 0.990. Then, the specific effects of APS process parameters were explored through Shapley additive explanations and sensitivity analysis. Principal component analysis was used to explain the constructed model and obtain the optimized area with a high bonding strength. After experimental validation, the results showed that under the APS process parameters of a current of 617 A, a voltage of 65 V, a H₂ flow of 3 L min^-1, and a thickness of 200 μm, the bonding strength increased by more than 19% to 55.5 MPa compared with the original maximum value of 46.6 MPa, indicating that the constructed GMM-SVR model can accurately predict the bonding strength of YSZ coating.