Multi-Scale Modeling for Predicting the Stiffness and Strength of Hollow-Structured Metal Foams with Structural Hierarchy

Deep Learning in Mechanical Metamaterials: From Prediction and Generation to Inverse Design

Mechanical metamaterials are meticulously designed structures with exceptional mechanical properties determined by their microstructures and constituent materials. Tailoring their material and geometric distribution unlocks the potential to achieve unprecedented bulk properties and functions. However, current mechanical metamaterial design considerably relies on experienced designers' inspiration through trial and error, while investigating their mechanical properties and responses entails time-consuming mechanical testing or computationally expensive simulations. Nevertheless, recent advancements in deep learning have revolutionized the design process of mechanical metamaterials, enabling property prediction and geometry generation without prior knowledge. Furthermore, deep generative models can transform conventional forward design into inverse design. Many recent studies on the implementation of deep learning in mechanical metamaterials are highly specialized, and their pros and cons may not be immediately evident. This critical review provides a comprehensive overview of the capabilities of deep learning in property prediction, geometry generation, and inverse design of mechanical metamaterials. Additionally, this review highlights the potential of leveraging deep learning to create universally applicable datasets, intelligently designed metamaterials, and material intelligence. This article is expected to be valuable not only to researchers working on mechanical metamaterials but also those in the field of materials informatics.

Influence of Film Coating Thickness on Secondary Electron Emission Characteristics of Non-Evaporable Getter Ti-Hf-V-Zr Coated Open-Cell Copper Foam Substrates

The application of vacuum materials with low secondary electron yield (SEY) is one of the effective methods to mitigate the electron cloud (EC). In this study, the Ti-Hf-V-Zr non-evaporable getter (NEG) film was deposited on open-cell copper foams with different pore sizes for the suppression of electron multipacting effects. Besides, the influence of the film thickness on the secondary electron emission (SEE) characteristics of Ti-Hf-V-Zr NEG film-coated open-cell copper foam substrates was investigated for the first time. The results highlighted that all uncoated and NEG-coated foamed porous copper substrates achieved a low SEY (<1.2), which reduced at least 40% compared to the traditional copper plates, and the foamed porous coppers with 1.34-μm-thick NEG coating had the lowest SEY. Moreover, the surface chemistry and the morphological and structural properties of foamed porous coppers of different pore sizes with and without Ti-Hf-V-Zr NEG films were systematically analyzed.

Numerical Investigation of Polymer Coated Nanoporous Gold

Nanoporous metals represent a fascinating class of materials. They consist of a bi-continuous three-dimensional network of randomly intersecting pores and ligaments where the ligaments form the skeleton of the structure. The open-pore structure allows for applying a thin electrolytic coating on the ligaments. In this paper, we will investigate the stiffening effect of a polymer coating numerically. Since the coating adds an additional difficulty for the discretization of the microstructure by finite elements, we apply the finite cell method. This allows for deriving a mesh in a fully automatic fashion from the high resolution 3D voxel model stemming from the 3D focused ion beam-scanning electron microscope tomography data of nanoporous gold. By manipulating the voxel model in a straightforward way, we add a thin polymer layer of homogeneous thickness numerically and study its effect on the macroscopic elastic properties systematically. In order to lower the influence of the boundary conditions on the results, the window method, which is known from homogenization procedures, is applied. In the second part of the paper, we fill the gap between numerical simulations and experimental investigations and determine real material properties of an electrolytic applied polypyrrole coating by inverse computations. The simulations provide an estimate for the mechanical properties of the ligaments and the polymeric coating and are in accordance with experimental data.