Enhanced Cellular Materials through Multiscale, Variable-Section Inner Designs: Mechanical Attributes and Neural Network Modeling

Abstract

In the current work, the mechanical response of multiscale cellular materials with hollow variable-section inner elements is analyzed, combining experimental, numerical and machine learning techniques. At first, the effect of multiscale designs on the macroscale material attributes is quantified as a function of their inner structure. To that scope, analytical, closed-form expressions for the axial and bending inner element-scale stiffness are elaborated. The multiscale metamaterial performance is numerically probed for variable-section, multiscale honeycomb, square and re-entrant star-shaped lattice architectures. It is observed that a substantial normal, bulk and shear specific stiffness increase can be achieved, which differs depending on the upper-scale lattice pattern. Subsequently, extended mechanical datasets are created for the training of machine learning models of the metamaterial performance. Thereupon, neural network (NN) architectures and modeling parameters that can robustly capture the multiscale material response are identified. It is demonstrated that rather low-numerical-cost NN models can assess the complete set of elastic properties with substantial accuracy, providing a direct link between the underlying design parameters and the macroscale metamaterial performance. Moreover, inverse, multi-objective engineering tasks become feasible. It is shown that unified machine-learning-based representation allows for the inverse identification of the inner multiscale structural topology and base material parameters that optimally meet multiple macroscale performance objectives, coupling the NN metamaterial models with genetic algorithm-based optimization schemes.

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

“…Indirect inverse design employs DL models to predict the mechanical properties of existing mechanical metamaterials, followed by the use of metaheuristic algorithms, such as evolution strategy and genetic algorithms, to filter out those with the desired properties. [76,77,[198][199][200] In semi-direct inverse design, DL models map the property space to the modeling parameters, requiring the use of additional modeling processes. [73,74,109,181] In contrast, direct inverse design uses DL models to straightforwardly generate geometries that meet user-defined properties, represented by pixel images or voxel volumes.…”

Tailoring 3D Star-Shaped Auxetic Structures for Enhanced Mechanical Performance

“…Opencelled porous structure metamaterials called lattice structures provide a special combination of high functionality and low weight. They facilitate the production of engineered components with tailored properties such as high specific strength and high toughness [4,5]. The low relative density as well as high surface area of such structures allow their use in filters, catalytic convertors, armour, heat exchangers, load-bearing components, biomedical implants, and so on.…”

A Numerical Tool for the Analysis of the Thermomechanical Behavior of Cavity Metamaterials Based on the Boundary Element Method

“…In order to obtain optimal metamaterial geometries, an accurate method for the effective property computation is required. Such, methods are numerical methods, mostly Finite Element Method, machine learning modeling and analytical methods [6,7].…”