Extreme Mechanics Letters最新文献

To address the demand of maintaining the structural configuration upon extreme temperature changes, metamaterials with high thermal-mechanical stability have attracted wide attention recently. However, there are still some challenges for previous studies regarding mechanical design and practical application exploration. This manuscript proposes the design strategy for the lattice sandwich metamaterial with excellent zero thermal-induced warping, along with the metamaterial-based antenna with a high stability of electromagnetic signal transmission. Through the design strategy of discrete lattice units of the metamaterial, the ultra-low thermal warping of the metamaterial is realized upon the non-uniform temperature field. Here, the theoretical model is established to predict both heat transfer and thermal-induced deformation behavior upon complex mechanical-thermal loading conditions. The combination of theoretical predictions, finite element analysis, and experiments verifies the thermal dimensional stability of the metamaterial proposed here. Compared with the heterogeneous bilayer plate, experimental thermal warping of the metamaterial specimen is reduced by 99.7%. Additionally, compared with the bilayer-based specimen, gain attenuation, the main lobe's offset angle, and the voltage standing wave ratio offset of the metamaterial-based antenna specimen are experimentally reduced by 99.5%, 99.9% and 74.2%, revealing the huge application potential of this metamaterial in the field of spacecraft communication.

In this paper, deep learning neural networks is used to predict the band structure of metamaterial lattices, and proactive inverse design is employed in bandgap modulation. A parametric design of the metamaterial lattice is proposed to achieve a rich design space. The corresponding band structure data is calculated by finite element method (FEM) to construct the data set. We successfully bypass complex theoretical or numerical methods to establish the mapping relationship between the lattice geometry parameters of metamaterials and the band structure data by constructing and training fully connected neural networks and convolutional neural networks (CNN). By combining the trained neural network model into an inverse design method of bandgap tuning, the geometric parameters of the metamaterial lattice can be obtained directly by inputting the target band structure. Finally, three object band structures are designed and verified by finite element simulation and experiment, which verifies the effectiveness of the inverse design method. This design approach can be extended to design other metamaterial properties.

Traditional tensile testing with “dogbone”-shaped specimen (ASTM E8, first standardized in 1924) strives for strain uniformity. Multiple tests with such samples help fit simple constitutive relation parameters on real materials. With the development of deep learning, the concept of employing entirely data-driven constitutive relations to capture more intricate material behavior has arisen. Nevertheless, these methods demand experimental data that are distributed throughout the complete stress–strain configuration space to effectively train the machine learning models. This is particularly crucial for mechanisms like hardening, which are time-dependent and sensitive to loading history. In this work, we investigate the potential to efficiently gather a wider range of experimental data points in the stress–strain configuration space using non-uniform samples and displacement-field mapping, leveraging advancements in computer vision techniques. We developed a metric to quantify stress state diversity in 2D tensile experiments and used it to optimize the shape of the sheet sample. The goal was to increase stress–strain diversity obtained within a single test, particularly in the linear elastic regime. Additional geometric constraints can be introduced on the design features, considering factors such as size and curvature to adapt to the microstructural characteristics of the sample material.

Locally resonant metamaterials can achieve unprecedented vibroacoustic performance by subwavelength distributions of small mechanical resonators on a host structure. Substantial broadband vibroacoustic attenuation can be achieved by multi-modal metamaterial panels, which exploit multiple translational and rotational resonator modes to manipulate the overall bending wave propagation. However, the multi-modal metamaterial concept has been studied only for idealized conditions, such as infinite panel extent and uniformly distributed resonators, limiting practical applicability. Efficient methodologies are still needed to study the behaviour of multi-modal metamaterial panels in real-world scenarios. In this work, this challenge is tackled by developing generalized effective medium models, i.e., homogenized material representations through equivalent macro-scale properties, tailored for finite-sized multi-modal metamaterial panels. For the special but important case of simply supported rectangular panels with uniformly distributed resonators, a dedicated analytical effective medium model is developed. For arbitrary boundary conditions and resonator distributions, effective medium finite elements are formulated. The diffuse sound transmission loss (STL) performance is efficiently predicted through Deterministic - Statistical Energy Analysis (Det-SEA), by coupling the effective medium model of the finite-sized metamaterial panel with a diffuse model of the surrounding sound fields. The proposed prediction approaches are validated against detailed FEM modelling, demonstrating that significant computational reductions are achieved while preserving accuracy. Results showcase that multi-modal metamaterial panels maintain broadband vibracoustic attenuation also when subjected to boundary effects and under partial metamaterial treatment.

Understanding the yielding and necking mechanisms of double network (DN) materials is crucial for establishing structure-property correlations. While previous studies have primarily focused on how macro-yielding behavior changes with network structure and swelling, in this work, we study the local internal damage in the pre-yielding process to unveil the yielding and necking mechanisms for typical DN gels. Through birefringence retardation imaging during tensile deformation on DN gels of various sample geometries, our findings reveal the following key points: 1) Initiation and Growth of Damage Zones: Prior to macroscopic yielding, damage zones initiate from the sample edges and gradually grow towards the sample center with elongation. 2) Rapid Propagation and Yielding: Beyond a certain stress threshold, damage zones rapidly propagate under constant loading. Two damage zones eventually merge at the sample center, resulting in yielding. 3) Intrinsic Stress Determination: The stress at this point is intrinsic and determined by the structure of the two networks. 4) Pseudo Size-Dependency: Samples with insufficient width exhibit a pseudo size-dependency of yielding stress, as yielding occurs before reaching the critical stress. To explain the origin of intrinsic yielding stress, we introduce an intrinsic effective crack length (c_I) as a measure of stress concentration around the internal crack tip of the damaged zone in the first network. Beyond this length, the influence on internal stress concentration around the damage zone is effectively screened by the load-bearing effect of the stretchable second network. The estimated c_I, dependent on the microstructure of the two networks, was approximately 10 times the mesh size of the first network for typical DN gels.

Lithium-nickel-manganese-cobalt-oxides (NMC) embedded in solid-electrolytes are being extensively applied as composite cathodes to match the high energy density of metallic anodes. During charge/discharge, the cathode composite often degrades through the evolution of micro-cracks within the grains, along the grain boundaries, and delamination at the particle-electrolyte interface. Experimental evidence has shown that regulating the morphology of grains and their crystallographic orientations is an effective way to relieve the volume-expansion-induced stresses and cracks, consequently stabilizing the electrochemical performance of the electrode. However, the interplay among the crystal orientation, grain morphology, and chemo-mechanical behavior has not been holistically studied. In that context, a thermodynamically consistent computational framework is developed to understand the role of microstructural modulation on the chemo-mechanical interactions of a polycrystalline NMC secondary particle embedded in a sulfide-based solid electrolyte. A phase-field fracture variable is employed to consider the initiation and propagation of cracks. A set of diffused phase-field parameters is adopted to define the transition of chemo-mechanical properties between the grains, grain boundaries, electrolyte, and particle-electrolyte interfaces. This modeling framework is implemented in the open-source finite element package MOOSE to solve three state variables: concentration, displacement, and phase-field damage parameter. A systematic parametric study is performed to explore the effects of aspect ratio, the crystal orientation of grains, and the interfacial fracture energy through the chemo-mechanical analysis of the composite electrode. The findings of this study offer predictive insights for designing solid-state batteries that provide stable performance with reduced fracture evolution.

In various applications of stretchable electronics and soft robots, soft materials are subjected to long-term load-bearing conditions, requiring them to possess good extreme mechanical properties such as highly stretchable, strong, fracture-resistant and anti-fatigue. Conventional soft materials with a single polymer network typically exhibit relatively low fracture resistance, while commonly employed toughening method involves introducing mechanical dissipation into original polymer networks often require complicated chemical synthesis and result in significant hysteresis under cyclic loads. Designing soft solid materials into soft network materials with periodic lattice structure paving another effective route to improve the fracture-resistant performance of soft materials, which has not been reported in the literature. This work provides a combined experimental and computational study on the mechanical properties and fracture behaviors of elastomer-based soft network materials with/without a precut crack under monotonic and cyclic loads, aiming to present a structural design strategy for fracture-resistant soft materials. The elastomer-based soft network materials are proven to be crack-insensitive, low-hysteresis and anti-fatigue. The nonlinear finite element analysis (FEA) is employed to reveal the underlying mechanism of crack-insensitivity in elastomer-based soft network materials. Both the mechanical properties and deformed configurations of soft network materials with/without a precut crack are accurately predicted by the FEA method. The effect of microstructure geometry and network topology on the mechanical properties of soft network materials is systematically studied. Under various amplitudes of cyclic applied strain, the elastomer-based soft network materials can survive even after individual microstructures ruptured, and the fatigue fracture process slows down as the applied strain amplitude decreases. In contrast to the behavior observed in monotonic loads where fracture initiates at the microstructure located ahead of the crack tip, fatigue fracture initiation in soft network materials with a precut crack exhibits a random distribution.

When a pair of parallel buckling beams of unequal thickness make lateral contact under increasing compression, eventually either the thin or the thick beam will snap, leading to collective motion of the beam pair. Using experiments and FEM simulations, we find that the distance $D$ between the beams selects which beam snaps first, and that the critical distance $D^{\ast }$ scales linear with the combined thickness of the two beams. To understand this behavior, we show that the collective motion of the beams is governed by a pitchfork bifurcation that occurs at strains just below snapping. Specifically, we use a model of two coupled Bellini trusses to find a closed form expression for the location of this pitchfork bifurcation that captures the linear scaling of $D^{\ast }$ with beam thickness. Our work uncovers a novel elastic instability that combines buckling, snapping and contact nonlinearities. This instability underlies the packing of parallel confined beams, and can be leveraged in advanced metamaterials.

Intermittent plastic flow known as the Portevin-Le Chatelier effect is a pronounced nonlinear phenomenon in a material science. One of the traditional approaches to the study of nonlinear system is to identify and analyze its responses to an external impulsive action. In the present work, the influence of impact indentation on the evolution of the spatio-temporal patterns of localized strain in an AlMg alloy deformed under the Portevin-Le Chatelier effect is studied with using an acoustic emission technique and high-speed video recording of propagating deformation bands. Dynamic and nonlinear responses to impact indentation of the surface of an alloy deformed by uniaxial tension above the conditional yield strength are revealed; the first include high values of dynamic hardness and local strain and loading rates, while the second involve the threshold and multiple nature of the force and acoustic responses. It has been established that there is a threshold impact energy of the indenter at which an induced strain jump of minimum amplitude occurs, accompanied by the formation of bands of macrolocalized plastic deformation. The conditions are established under which the work of plastic deformation during the development of the induced strain jump significantly, by more than two orders of magnitude, exceeds the energy of the initiating impact, which acts only as a trigger for the “premature” release of the elastic energy accumulated in the material before the moment of impact. It is shown that the impact-induced bands of macrolocalized deformation represent a hidden three-dimensional type of erosion damage that reduces the mechanical stability and durability of the alloy. A phenomenological model is proposed for the formation of bands, strain jumps, and bursts of acoustic emission induced by an indenter impact, which qualitatively explains the experimental results obtained in the work.

Achieving precise control over interlayer interactions and properties in van der Waals (vdW) bilayers represents a significant breakthrough in materials science. In this study, using molecular dynamic and a hybrid model we show an emergent coupling between interlayer distance and twist angle in the reconstructed twisted bilayer graphene (tBLG). The reconstructed state of tBLG arises from the delicate interplay between vdW interlayer interactions and elastic energy within each layer, leading to the emergence of a cross term in the average total energy density of tBLG concerning interlayer distance and twist angle. Such cross term enables tunable interlayer distance via twist and local rotation via interlayer distance. The average interlayer distance in tBLG undergoes an increase from 3.37 Å to 3.45 Å within the range of twist angles from 0° to 4°. Our investigation unveils that the coupling originates from regions of high-energy stacking with maximum interlayer distance increase with the twist angle due to atomic reconstruction. This coupling phenomenon is not exclusive to tBLG, as it appears in other vdW bilayers like MoS₂/MoS₂, MoSe₂/MoSe₂, WS₂/WS_2, and WSe₂/WSe₂. The coupled interlayer interaction between interlayer distance and twist would have implications for tailoring 2D vdW materials for various applications.