Design of double layer protective coatings: Finite element modeling and machine learning approximations

Thin film-composed coatings can significantly improve the mechanical and tribological properties of spacecraft friction units. In this regard, protection by ion-plasma coatings has become one of the most used options and well-established technologies. However, design of coatings that remain effective over multiple usages, is a challenge. Improving their production requires a large number of parameters to be optimized and many verification experiments to be set up. We exploited machine learning algorithm to solve the problem of double layer coatings optimization with respect to a set of mechanical properties (hardness, Young's modulus, Poisson's ratio, and yield stress). The training dataset was constructed using the adaptive sampling algorithm and numerical simulation of indentation in ANSYS for coatings of different compositions and thicknesses. The machine learning approximation provided significant accuracy (R 2 > 0.96) in predicting the coating hardness based on the mechanical properties of its individual layers. The feature importance analysis of the Extra Trees algorithm was used to define the parts of the indentation curve that carry information about the properties of individual coating layers. We have also addressed the inverse problem of coatings design in terms of the required hardness. Our findings demonstrate that only tandem approach to the cross-validation task permitted correct estimation of error in the presence of multiple ambiguous solutions. The value of error was smaller than 0.15 GPa. Being comprehensive, the proposed methodology can be applied to design optimal multilayer coatings in terms of their mechanical properties for friction units in aviation and rocketry-astronautics. • Database of numerical nanoindentation experiments for double layered coatings. • Direct problem of predicting coating hardness from layer's parameters was solved. • Extra Trees algorithm has high performance for predicting hardness: R2 score >0.95 • Inverse problem of predicting layer's parameters from coating hardness was solved. • Tandem approach was important during the cross-validation in the inverse problem.