Data-Driven-Design: A Novel Approach for Use-Inspired Multi-material Design of Architectured Lattices

Multimaterial additive manufacturing (MAM) has introduced a new dimension of capability and versatility in the already revolutionary field of 3D printing through enabling fabrication of custom geometries via multiple materials. The versatility of MAM techniques renders an exceedingly high-dimensional set of possible multi-material designs for a given geometry or structure This vast space of realizable designs, in turn, calls upon a systematic explore-and-select framework that accurately accounts for both the mechanics and the functionality of the resultant product. We propose a constitutive model-free data-driven-design (D³) approach for optimal designs of multi-material lattices here, where we directly utilize data to find the optimal selection of materials in a use-inspired manner. In this talk, after presenting the general framework for multi-material design, we will focus on the viscoelastic lattices and showcase our approach for designing multi-material architectured lattices with maximal dissipation. In one line of efforts, we demonstrate that D³ enables designing multi-material three-dimensional unit-cells for four different affine deformation patterns. We provided our framework with the dynamic viscoelastic data for 25 different materials as variants of Epoxy, ABS, and TB+. We conduct optimal designs for both single-frequency and multi-frequency cases, and show that a multi-material design, by orchestrating the heterogeneities and corresponding deformations, can mirror the dissipation of a homogeneous lattice made of the most dissipative material. Separately, we investigate multi-material design of a finite lattice under a non-uniform loading with a similar objective of maximizing mechanical dissipation. In contrast with a lattice homogeneously comprised of the most dissipative material, we demonstrate that D³ provides an optimal design with 3-fold enhancement in dissipation. We conclude by discussing how our framework naturally generalizes to multi-physics and multi-objective metastructure design, offering a unified, data-driven approach to optimal material selection under complex constraints.