Mechanical performance of zirconia-silica bilayer coating on aluminum alloys with varying porosities: Deep learning and microstructure-based FEM

Abstract

Despite significant efforts in multiscale modeling of multiphase materials, the effective mechanical response of the thin surface layers still remains elusive due to the complexity of heterogeneous microstructures and composition. This work presents a multiscale modeling approach for determining the effective elastic properties and mechanical behavior of heterogeneous materials such as thin multilayered oxides. This approach is specifically tailored for identifying the mechanical performance of porous solid oxides as the outermost layer. A generative adversarial network (GAN) is used to generate microstructure with varying porosities, complex pore shapes, and randomly distributed pores. The multiscale modeling framework is based on the Mechanics of Structure Genome (MSG) to concurrently derive micromechanics and structural analysis models from heterogeneous structures. We have evaluated the modeling approach based on the available experimental data in the literature. By resorting to the presented framework, not only the effective mechanical properties but also the global and local fields within the macro- and micro-structures, respectively, can be quantitatively predicted for a wide range of heterogeneous systems. A thorough investigation of the mechanical performance of aluminum alloys with deposited porous ZrO2 – SiO2 bilayer is presented to demonstrate the efficacy and applicability of this approach. Results show that the elastic properties of silica and zirconia are dramatically reduced when porosity exceeds 5% and 3%, respectively.