Geometrical recognition of metallic foam microstructures using a deep learning approach

Abstract

The geometric characteristics of nanostructured porous metallic foams (NMFs) impact their properties, enabling applications in electrocatalysis, energy conversion, and energy storage. The morphology of these three-dimensional (3D) structures is complex, in large part, because of their complicated formation mechanisms. This work presents a computational strategy for geometrically characterizing different NMFs designs. Three types of samples were synthesized using the dynamic hydrogen bubble template electrodeposition method. A set of microscopic confocal images of NMFs was used as training input samples. Herein, a variational autoencoder (VAE) was adjusted under a pretext task, allowing learning embedding descriptors to represent the geometry of microscopic observations. To evaluate the feasibility of using a VAE to assist in the classification of NMFs, a set of machine learning classifiers was implemented to discriminate among the groups of embedded vectors according to their classes. Furthermore, the capacity of the VAE was validated regarding the capability to separate the vectors according to their classes. Explainability mechanisms stand out geometrical features of input images that had major support during the classification task.