  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.
