Extreme Mechanics Letters最新文献

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.

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.