  Composite materials with 3D architectures are desirable in a variety of
applications for the capability of tailoring their properties to meet multiple
functional requirements. By the arrangement of materials' internal components,
structure design is of great significance in tuning the properties of the
composites. However, most of the composite structures are proposed by empirical
designs following existing patterns. Hindered by the complexity of 3D
structures, it is hard to extract customized structures with multiple desired
properties from large design space. Here we report a multi-objective driven
Wasserstein generative adversarial network (MDWGAN) to implement inverse
designs of 3D composite structures according to given geometrical, structural
and mechanical requirements. Our framework consists a GAN based network which
generates 3D composite structures possessing with similar geometrical and
structural features to the target dataset. Besides, multiple objectives are
introduced to our framework for the control of mechanical property and isotropy
of the composites. Real time calculation of the properties in training
iterations is achieved by an accurate surrogate model. We constructed a small
and concise dataset to illustrate our framework. With multiple objectives
combined by their weight, and the 3D-GAN act as a soft constraint, our
framework is proved to be capable of tuning the properties of the generated
composites in multiple aspects, while keeping the selected features of
different kinds of structures. The feasibility on small dataset and potential
scalability on objectives of other properties make our work a novel, effective
approach to provide fast, experience free composite structure designs for
various functional materials.
