Extreme Mechanics Letters最新文献

Soft materials such as rubbers and soft tissues often undergo large deformations and experience damage degradation that impairs their function. This energy dissipation mechanism can be described in a thermodynamically consistent framework known as continuum damage mechanics. Recently, data-driven methods have been developed to capture complex material behaviors with unmatched accuracy due to the high flexibility of deep learning architectures. Initial efforts focused on hyperelastic materials, and recent advances now offer the ability to satisfy physics constraints such as polyconvexity of the strain energy density function by default. However, modeling inelastic behavior with deep learning architectures and built-in physics has remained challenging. Here we show that neural ordinary differential equations (NODEs), which we used previously to model arbitrary hyperelastic materials with automatic polyconvexity, can be extended to model energy dissipation in a thermodynamically consistent way by introducing an inelastic potential: a monotonic yield function. We demonstrate the inherent flexibility of our network architecture in terms of different damage models proposed in the literature. Our results suggest that our NODEs re-discover the true damage function from synthetic stress-deformation history data. In addition, they can accurately characterize experimental skin and subcutaneous tissue data.

The signature topological feature of Maxwell lattices is their polarization, which manifests as an unbalance in stiffness between opposite edges of a finite domain. The manifestation of this asymmetry is especially dramatic in the case of soft lattices undergoing large nonlinear deformation under concentrated loads, where the excess of softness at the soft edge can result in the activation of sharp indentations. This study explores how this mechanical dichotomy between edges can be tuned and possibly extremized by working with soft magneto-mechanical metamaterials. The magneto-mechanical coupling is obtained by endowing the lattice sites with permanent magnets, which activate a network of magnetic forces that can interact with – either augmenting or competing with – the elasticity of the material. Specifically, under sufficiently large deformation that macroscopically alters the equilibrium positions of the sites, the attractive forces between the magnets can trigger bistable reconfiguration mechanisms. The strength of such mechanisms depends on the landscapes of elastic reaction forces exhibited by the edges, which are different due to the polarization, and is therefore inherently edge-selective. We show that, on the soft edge, the addition of magnets simply enhances the softness of the edge. In contrast, on the stiff edge, the magnets activate snapping mechanisms that locally reconfigure the cells and produce a lattice response reminiscent of plasticity, characterized by residual deformation that persists upon unloading.

Glass was generally considered to be brittle, and its applications were significantly limited by its vulnerability to fracture caused by deformation. The folding tests with ultrathin glass (UTG) conducted in this work illustrate that glass can also deform plastically and generate permanent creases on a macroscopic level. Moreover, the plastic deformation can gradually and partially recover at room temperature and the level of recovery can be inhibited by a longer holding time or through repeated loading. Based on the experimental observation, a phenomenological model is established to predict the plastic behavior of the concerned glass and we further discuss the possible cause of plastic deformation and its recovery and the potential applications.

Fatigue is a critical failure mechanism in various materials, often leading to catastrophic consequences. Designing materials with non-catastrophic fatigue failure is desirable yet challenging. This work presents the remarkable fatigue behavior of densified wood, exhibiting both a higher fatigue strength and non-catastrophic failure compared to natural wood. The improved bonding between wood fibers, primarily through hydrogen bonds, enables robust structural integrity even after fatigue failure. This mechanistic understanding offers insights for achieving non-catastrophic fatigue failure in diverse materials, presenting a fundamental principle for material design with broad implications.

Dislocation slip-based crystal plasticity models have been a great success in connecting the fundamental physics with the macroscopic deformation of crystalline materials. Pioneered by Taylor in his work on “plastic strain in metals” (Taylor, 1938), and further advanced by Bishop and Hill (1951a, 1951b), the Taylor–Bishop–Hill theory laid the foundation of today’s constitutive models on crystal plasticity. An intriguing part of those modeling is to determine the active slip systems—which system to be involved in and how much it contributes to the deformation. In this paper, we developed a machine learning-based algorithm to determine accurately and efficiently the active slip systems in crystal plasticity constitutive models. Applications to the common three polycrystalline metals, face-centered cubic (FCC) copper, body-centered cubic (BCC) α-iron, and hexagonal close-packed (HCP) AZ31B, demonstrate that even a simple neural network could give rise to accurate and efficient results in comparing with traditional routines. There seems to be plenty of space for further reducing the computation time and hence scaling up the simulating samples.

MXene exhibits outstanding electrical conductivity, but its susceptibility to oxidation can impede its conductivity potential. While there is extensive research on the electrical, mechanical properties, and fracture behavior of pure MXene, the exploration of the oxidized MXene is rare, especially for the commonly observed Ti₃C₂-TiO₂ mixtures. In this study, we conducted molecular dynamics (MD) and Density Functional Theory (DFT) approaches and, for the first time, discovered three stable crystal structures of pure MXene with attached TiO₂ layers: Loose, Comb, and Tight. For each of these structures, we investigated the anisotropic mechanical and fracture behaviors based on two loading scenarios: ribbon and pre-cracked single layers. The results indicate that the anisotropic behavior is predominantly manifested in Loose and Tight structures. The structural asymmetry of Comb results in a larger and evolving cohesive zone. The direction of the TiO₂ layer-MXene interface bonds influences the material's strength, with the Tight structure exhibiting the highest resistance to fracture.

In this work, we introduce a novel three-dimensional auxetic mechanical metamaterial consisting of rotating tetrahedra connected by ideal hinges at their vertices, arranged to form a periodic framework structure. Through analytical and kinematic approaches, we evaluated the deformation behavior of the proposed idealized model, revealing Poisson’s ratios ranging between −0.36 and −2.72 and a transverse isotropic response as a result of its geometry. A specimen of the proposed metamaterial concept is designed by introducing deformable ribs between the solid units, and fabricated via additive manufacturing in polymeric material. Auxetic behavior of the prototype was assessed through a compression test and accurately predicted by a full-scale Finite Element model. We envisage that this new metamaterial design can have a significant impact on a wide range of engineering applications, particularly as bone substitute biomaterial.

In the field of soft robotics, flexibility, adaptability, and functionality define a new era of robotic systems that can safely deform, reach, and grasp. To optimize the design of soft robotic systems, it is critical to understand their configuration space and full range of motion across a wide variety of design parameters. Here we integrate extreme mechanics and soft robotics to provide quantitative insights into the design of bio-inspired soft slender manipulators using the concept of reachability clouds. For a minimal three-actuator design inspired by the elephant trunk, we establish an efficient and robust reduced-order method to generate reachability clouds of almost half a million points each to visualize the accessible workspace of a wide variety of manipulator designs. We generate an atlas of 256 reachability clouds by systematically varying the key design parameters including the fiber count, revolution, tapering angle, and activation magnitude. Our results demonstrate that reachability clouds not only offer an immediately clear perspective into the inverse problem of control, but also introduce powerful metrics to characterize reachable volumes, unreachable regions, and actuator redundancy to quantify the performance of soft slender robots.

Topologically interlocking material (TIM) systems offer adjustable bending stiffness controlled by external pre-stress, as shown in previous studies. This study focuses on a specific TIM system comprised of truncated tetrahedral particles interconnected via tensioned wires. The fabrication process involves weaving nylon wires through 3D printed truncated tetrahedrons that have longitudinal and latitudinal through-holes. By varying the tension applied to the wires, one can systematically control the overall bending stiffness of the TIM system. We change the surface friction and the contact angle between adjacent particles at a fixed wire tension, to study experimentally how they affect the system’s bending response. We inform experiments with Level Set Discrete Element Method (LS-DEM) simulations, to correlate surface friction and contact area changes with the system’s bending modulus. The numerical model is shown to be predictive and could be used in the future to evaluate designs of TIMs.

Designing chemical molecules that target the skin for non-invasive transdermal drug delivery is of significant interest for both wound healing and skincare applications. These skin-targeting molecules must permeate the outermost protective layer of the skin, the stratum corneum (SC), which consists of dead corneocytes embedded in a lipid matrix, to fulfill their biological functions. Adsorption onto and diffusion through the lipid matrix in the SC represent two key steps for the successful permeation of a skin-targeting molecule across the SC into the underlying skin layers. Here we compare the effects of cyclization and palmitoylation on the adsorption and diffusion of a short polar peptide across a model SC lipid bilayer using molecular dynamics simulations. The cyclized peptide showed slightly better binding to the SC lipid bilayer and similar interaction energies with SC lipids compared to the unmodified peptide. In contrast, the palmitoylated peptide exhibited much stronger interaction with SC lipids via insertion of its attached fatty acid tail into the SC lipid bilayer. The average diffusivity of the cyclized peptide across the SC lipid bilayer was approximately twice that of the unmodified peptide, whereas the palmitoylated peptide’s diffusivity was about 2.7 times higher. Thus, palmitoylation appears to be a promising strategy for enhancing the binding and permeability of short polar peptides across the SC lipid matrix.