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. The performance of these structures heavily relies on
the detailed design of mesostructural elements. Many current approaches use data-driven design
to generate multi-lattice transition regions, making use of models that jointly address the geometry
and properties of the mesostructures. However, it remains unclear whether the integration of
mechanical properties into the data set for generating multi-lattice interpolations is beneficial
beyond geometry alone. To address this issue, this work implements and evaluates a hybrid
geometry/property machine learning model for generating multi-lattice transition regions. We
compare the results of this hybrid model to results obtained using a geometry-only model. Our
research determined that incorporating physical properties decreased the number of variables to
address in the latent space, and therefore improves the ability of generative models for developing
transition regions of multi-lattice structures.Mechanical Engineerin
