Multi-Lattice Topology Optimization Via Generative Lattice Modeling

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Abstract Additive manufacturing enables the fabrication of multi-lattice structures, an advanced design approach featuring heterogeneous lattices at the mesoscale which are arranged to achieve a diverse and purposeful distribution of material properties at the macroscale. Compared to uniform lattice structures, multi-lattice structures permit greater design freedom and a larger design space, which makes it possible to achieve superior structure performance. However, the expanded design space introduces a substantial increase in the complexity that must be managed in order to achieve a multi-lattice structure solution. However, there is a lack of design automation approaches that can tractably create multi-lattice structures. This article introduces an innovative multi-scale topology optimization (TO) framework, called multi-lattice topology optimization with variational autoencoder (MulaTOVA), that is capable of concurrently addressing macro- and mesoscale design requirements. Neural networks (NNs) are employed in this framework to jointly represent the structural topology at the macroscale and the lattice heterogeneity at the mesoscale, enabling simultaneous optimization through the updating of the NNs’ weights. The connectivity between lattices is implicitly constrained by constraining the NNs, while the diversity of the lattices is guaranteed through a generative lattice model which is trained over a large lattice dataset. The performances of various NN types are compared, and Fourier neural operators (FNOs) demonstrated the best flexibility in balancing lattice diversity and local connectivity. Furthermore, our results show that the multi-lattice TO structures achieve a higher stiffness-to-weight ratio than solid TO structures.