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

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.