学术文献库

With the achievement on the additive manufacturing, the mechanical properties of architectured materials can be precisely designed by tailoring microstructures. As one of the primary design objectives, the elastic isotropy is of great significance for many engineering applications. However, the prevailing experimental and numerical methods are normally too costly and time-consuming to determine the elastic isotropy of architectured materials with tens of thousands of possible microstructures in design space. The quick mechanical characterization is thus desired for the advanced design of architectured materials. Here, a deep learning-based approach is developed as a portable and efficient tool to identify the elastic isotropy of architectured materials directly from the images of their representative microstructures with arbitrary component distributions. The measure of elastic isotropy for architectured materials is derived firstly in this paper to construct a database with associated images of microstructures. Then a convolutional neural network is trained with the database. It is found that the convolutional neural network shows good performance on the isotropy identification. Meanwhile, it exhibits enough robustness to maintain the performance under fluctuated material properties in practical fabrications. Moreover, the well-trained convolutional neural network can be successfully transferred among different types of architectured materials, including two-phase composites and porous materials, which greatly enhance the efficiency of the deep learning-based approach. This study can give new inspirations on the fast mechanical characterization for the big-data driven design of architectured materials.