Extreme Mechanics Letters最新文献

Generative artificial intelligence (AI) is shown to be a useful tool to automatically learn from existing information and generate new information based on their connections, but its usage for quantitative mechanical research is less understood. Here, we focus on the structure-mechanics relationship of architected graphene as graphene with void defects of specific patterns. We use Molecular Dynamics (MD) to simulate uniaxial tension on architected graphene, extract the von Mises stress field in mechanical loading, and use the results to train a fine-tuned generative AI model through a Low-Rank Adaptation method. This model enables the freely designed architected graphene structures and predicts its associated stress field in uniaxial tension loading through simple descriptive language. We demonstrate that the fine-tuned model can be established with a few training images and can quantitatively predict the stress field for graphene with various defect geometries and distributions not included in the training set. We validate the accuracy of the stress field with MD simulations. Moreover, we illustrate that our generative AI model can predict the stress field from a schematic drawing of the architected graphene through image-to-image generation. These features underscore the promising future for employing advanced generative AI models in end-to-end advanced nanomaterial design and characterization, enabling the creation of functional, structural materials without using complex numerical modeling and data processing.

Spider dragline silk is one promising material for producing artificial muscles, owing to its remarkable capacity for supercontraction and twist when exposed to high humidity. Based on the hydration absorption equation and the standard reinforcing model, we develop a phenomenological theory for elucidating the hydration-induced supercontraction and twist of spider dragline silk. The theory can reasonably predict the responses of softening, anisotropy, hydration-supercontraction, and twist of spider dragline silk. The theoretical predictions align with the experimental results. This study provides valuable insight into the underlying mechanisms of the hydration-induced deformation of spider dragline silk.

Interpenetrating Phase Composite (IPC) metamaterials, based on lattice topologies, have garnered significant attention as advanced materials for structural applications. However, conventional IPCs, which rely on periodic lattice unit cells, are prone to catastrophic failure due to their global deformation modes. To overcome this limitation, we present a novel IPC design utilizing aperiodic truss unit cells, inspired by the elusive “Einstein” monotile pattern. Our concept is demonstrated through IPC 3D printed via polymer jetting, using a hard polymer as the lattice filler and a soft polymer as the matrix. The distinctive mechanical properties of IPCs are characterized through single and cyclic quasi-static compression testing. Our findings demonstrate that aperiodic IPCs enable progressive deformation with gradual compression stress plateaus. Additionally, aperiodic IPCs exhibit remarkable damage tolerance, retaining 67.59 % of residual energy absorption and 73.83 % of ultimate strength after multiple cyclic compressions up to 30 % strain. These mechanisms are attributed to the synergistic deformation of interconnected unit cells, which lead to self-adjusting plastic collapse, progressive displacement evolution and delocalized deformation. This aperiodic concept paves the way for developing high-performance cushioning protection materials.

Elastic wave sensing is a crucial information acquisition technology with extensive applications in structural health monitoring, nondestructive testing, and other fields. However, traditional elastic wave sensing systems face challenges such as poor performance, high power consumption, and limited adaptability in complex environments. Here, a robust elastic wave sensing system integrating disordered metasurface and deep learning is demonstrated, enhancing the sensing performance in the environments with harsh noise or unknown signals. The scheme fully utilizes the complementary advantages of disordered metasurface and deep learning in physical encoding and intelligent decoding respectively. The meticulously designed disordered metasurface efficiently encodes elastic waves, and a single sensor acquires the encoding signals, enabling low-power information acquisition. The deep learning model performs adaptive and rapid intelligent decoding of the encoding signals, achieving efficient and robust information sensing while overcoming the sensing limitations of traditional compressed sensing in complex scenarios with low SNR and unknown signals. A series of experimental results demonstrate that, even under severe noise interference (known signal $\mathrm{SNR}\ge -15\mathrm{dB}$, unknown signal $\mathrm{SNR}\ge -7\mathrm{dB}$), the system can sense location information in elastic waves with a millisecond-level sensing speed and an accuracy above 90%. Furthermore, the successful application of the sensing system in vibration-tracking imaging and mechanical reading–writing further validates its practicability and robustness. This work may open up new avenues for the potential application of intelligent sensing in the fields of structural health monitoring, nondestructive testing, and human–machine interaction.

An inf–sup stable FE formulation for the thermo-chemo-mechanical simulation of thermoresponsive hydrogels is herein proposed by approximating the displacement field via quadratic shape functions and both the chemical potential (fluid pressure) and the temperature fields by linear functions. The formulation is implemented into a stable thermo-chemo-mechanical user-element subroutine (UEL) in Abaqus, denoted as Q2Q1Q1. The proposed formulation has been validated in relation to thermoresponsive hydrogels to interpret several examples of transient diffusion-driven swelling deformations. First, the upper/lower critical solution temperature behaviors of thermoresponsive hydrogels has been captured, studying several peculiarities comprising the diffusion length influence at the instantaneous loading state and the overlooked influence of the mass flux and the hyperelastic stretching on the temperature field. Subsequently, numerical analysis have been conducted in order to investigate the impact of temperature-dependent swelling ratio on the mechanical behavior of spheres undergoing compression. The accuracy of the proposed formulation has been assessed by numerically replicating the seminal experiments that explore the influence of crosslinking density on the thermally driven swelling of PNIPAAm hydrogels.

Impact performance is a key consideration when designing objects to be encountered in everyday life. Unfortunately, how a structure absorbs energy during an impact event is difficult to predict using traditional methods, such as finite element analysis, because of the complex interactions during high strain-rate compression. Here, we employ a physics-based model to predict impact performance of structures using a single quasistatic experiment and refine that model using intermediate strain rate and impact testing to account for strain-rate dependent strengthening. This model is trained and evaluated using experiments on additively manufactured generalized cylindrical shells. Using transfer learning, the trained model can predict the performance of a new design using data from a single quasistatic test. To validate the transfer learning model, we extrapolate to new impactor masses, new designs, and a new material. The accuracy of this model allows researchers to quickly screen new designs or leverage pre-existing databases of quasistatic test data. Furthermore, when impact tests are necessary to validate design selection, fewer impact tests are necessary to identify optimal performance.

Topology optimization algorithms often employ a smooth density function to implicitly represent geometries in a discretized domain. While this implicit representation offers great flexibility to parametrize the optimized geometry, it also leads to a transition region. Previous approaches, such as the Solid Isotropic Material Penalty (SIMP) method, have been proposed to modify the objective function aiming to converge toward integer density values and eliminate this non-physical transition region. However, the iterative nature of topology optimization renders this process computationally demanding, emphasizing the importance of achieving fast convergence. Accelerating convergence without significantly compromising the final solution can be challenging. In this work, we introduce a machine learning approach that leverages the message-passing Graph Neural Network (GNN) to eliminate the non-physical transition zone for the topology optimization problems. By representing the optimized structures as weighted graphs, we introduce a generalized filtering algorithm based on the topology of the spatial discretization. As such, the resultant algorithm can be applied to two- and three-dimensional space for both Cartesian (structured grid) and non-Cartesian discretizations (e.g. polygon finite element). The numerical experiments indicate that applying this filter throughout the optimization process may avoid excessive iterations and enable a more efficient optimization procedure.

Cutting mechanics in soft solids have been a subject of study for several decades, an interest fuelled by the multitude of its applications, including material testing, manufacturing, and biomedical technology. Wire cutting of a parallelepiped sample is the simplest model system to analyse the cutting resistance of a soft material. However, even for this simple system, the complex failure mechanisms that underpin cutting are still not completely understood. Several models that connect the critical cutting force to the radius of the wire and the key mechanical properties of the cut material have been proposed. An almost ubiquitous simplifying assumption is a state of plane (and anti-plane) strain in the material. In this paper, we show that this assumption can lead to erroneous conclusions because even such a simple cutting problem is essentially three-dimensional. A planar approximation restricts the analysis to the stress distribution in the midplane of the sample. However, through threedimensional finite element modelling, we reveal that the maximal tensile stress – and thus the likely location of cut initiation – is located in the front face of the sample (end effect). Friction reduces the magnitude of this tensile stress, but this detrimental effect can be counteracted by large “slice-to-push” (shear-to-indentation) ratios. The introduction of the “end effect” helps reconcile a recent controversy around the role of friction in wire cutting, for it implies that slicing can indeed reduce required cutting forces, but only if the slice-push ratio and the friction coefficient are sufficiently large.

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.