Multiscale Experiments and Predictive Modelling for Inverse Design and Failure Mitigation in Additively Manufactured Lattices (2402.16452v1)

Abstract: Additive manufacturing (AM) enables the development of high-performance architected cellular materials, emphasizing the growing importance of establishing programmable and predictable energy absorption capabilities. This study evaluates the impact of a precisely tuned fused filament fabrication (FFF) AM process on the energy absorption and failure characteristics of thermoplastic lattice materials through multiscale experiments and predictive modelling. Lattices with four distinct unit cell topologies and three varying relative densities are manufactured, and their in-plane mechanical response under quasi-static compression is measured. Macroscale testing and micro-CT imaging reveal relative density-dependent damage mechanisms and failure modes, prompting the development of a robust predictive modelling framework to capture process-induced performance variation and damage. For lower relative density lattices, an FE model based on the extended Drucker-Prager material model, incorporating Bridgman correction with crazing failure criteria, accurately captures the crushing response. As lattice density increases, interfacial damage along bead-bead interfaces becomes predominant, necessitating the enrichment of the model with a microscale cohesive zone model to capture interfacial debonding. All proposed models are validated, highlighting inter-bead damage as the primary factor limiting energy absorption performance in FFF-printed lattices. Finally, the predictive modelling introduces an enhancement factor, providing a straightforward approach to assess the influence of the AM process on energy absorption performance, facilitating the inverse design of FFF-printed lattices. This approach enables a critical evaluation of how FFF processes can be improved to achieve the highest attainable performance and mitigate failures in architected cellular materials.