Structural Metamaterials: Poster Session
Sponsored by: TMS Materials Processing and Manufacturing Division, TMS Structural Materials Division, TMS: Additive Manufacturing Committee, TMS: Mechanical Behavior of Materials Committee
Program Organizers: Amy Wat, Lawrence Livermore National Laboratory; Brad Boyce, Sandia National Laboratories; Xiaoyu Zheng, University of California, Los Angeles; Fabrizio Scarpa, University of Bristol; Robert Ritchie, University of California, Berkeley
Monday 5:30 PM
February 28, 2022
Room: Exhibit Hall C
Location: Anaheim Convention Center
G-32: Coated Nano- and Micro-lattices via Magnetron Sputtering: Adie Alwen1; Alina Garcia Taormina1; Andrea Hodge1; 1University of Southern California
Magnetron sputtering has emerged as a promising and versatile deposition method for coating nano- and micro-lattices to produce novel core-shell composite structures. By leveraging sputtering’s expansive material workspace and its ability to tailor coating microstructure, this work aims to achieve unrealized combinations of lattice geometry, composition, microstructure, and feature size to further functionalize these advanced hierarchical materials. Previous work has demonstrated that sputtering can effectively introduce new ceramic, metallic, and alloyed materials onto lattice structures; however, coating gradients and morphological variations have also been observed. This research seeks to further develop core-shell composite nano- and micro-lattices by examining the influence of sputtering parameters, cathode geometry, and deposition line-of-sight on the resulting coating characteristics on these complex 3D structures. Creating a foundational understanding of sputter deposition on nano- and micro-lattice materials will enable more accurate design and synthesis of novel architected core-shell composites.
G-33: Gaussian Process Regression as a Surrogate Model for the Computation of Dispersion Relations: Alexander Ogren1; Berthy Feng1; Katherine Bouman1; Chiara Daraio1; 1California Institute of Technology
Periodic structured materials, constructed by tessellating identical unit cells in arrays, can be designed to exhibit unique and useful elastic wave propagation behavior (e.g. reflection, lensing, wave guiding). The propagation of waves in a periodic structured material is described by its dispersion relation, a function relating a wave’s frequency to its wavevector. However, the evaluation of dispersion relations, particularly in higher dimensions, is computationally expensive. To alleviate this cost, we demonstrate that Gaussian process regression can be used as a surrogate model in the computation of dispersion relations of periodic structured materials.In contrast to existing neural network based surrogate models for dispersion computations, our approach requires very little training data, has a dynamically tunable accuracy-cost balance, and can be applied to material designs of any representation (pixelated, parametric, etc.). Additionally, our model can make dispersion predictions at infinite resolution, and can efficiently be used in gradient based topology optimization.