  Additive manufacturing has revolutionized structural optimization by
enhancing component strength and reducing material requirements. One approach
used to achieve these improvements is the application of multi-lattice
structures, where the macro-scale performance relies on the detailed design of
mesostructural lattice elements. Many current approaches to designing such
structures use data-driven design to generate multi-lattice transition regions,
making use of machine learning models that are informed solely by the geometry
of the mesostructures. However, it remains unclear if the integration of
mechanical properties into the dataset used to train such machine learning
models would be beneficial beyond using geometric data alone. To address this
issue, this work implements and evaluates a hybrid geometry/property
Variational Autoencoder (VAE) for generating multi-lattice transition regions.
In our study, we found that hybrid VAEs demonstrate enhanced performance in
maintaining stiffness continuity through transition regions, indicating their
suitability for design tasks requiring smooth mechanical properties.
