Abstract
With the development of the economy and society, porous materials have been widely used in various fields due to their unique structure and function. Therefore, it is of great significance to analyze the mechanical properties of porous materials. In traditional analysis methods, experimental and numerical simulation methods are mainly used. When conducting finite element numerical simulation analysis on porous materials, a large number of fine grids need to be divided, and the calculation process is time-consuming and laborious. This article randomly generates porous microstructure models through algorithms and uses efficient quadtree algorithms to calculate their mechanical properties, thereby obtaining a large amount of machine-learning sample data. Furthermore, a neural network-based machine learning algorithm is established to predict the mechanical properties of porous materials. By using microstructure images as the input layer of the model, the mechanical properties under corresponding conditions can be directly predicted. This study provides a new method for predicting mechanical properties based on microstructure images. It has been verified that the mechanical properties directly predicted by the network are similar to the actual ones, with high accuracy and computational efficiency.