Extreme Mechanics Letters最新文献

Biomimetic metamaterials have gained increasing attention due to their exceptional characteristics such as high toughness, robust strength, and effective noise reduction. However, their complex and irregular nature presents challenges in tailoring their mechanical properties for specific applications. This study proposes a novel dual-network approach to overcome these challenges. The approach involves creating a forward model to accurately predict the mechanical properties and interconnectivity of the metamaterial without the need for growth and homogenization processes. Additionally, an inverse model is utilized to accurately predict designs for desired anisotropic stiffness. Compared to traditional bidirectional networks, our approach demonstrates superior accuracy in designing elastic properties. Our results also show that the metamaterial exhibits a broad low-frequency response while maintaining exceptional load-carrying capacity, making it a promising solution for designing low-frequency vibration suppression metamaterials.

Fiber-reinforced composites with Bouligand structure exhibit remarkable mechanical properties due to the intricate arrangement of fibers. In this study, we propose a coarse-graining (CG) model specifically developed to capture the behavior of Bouligand structures. The model incorporates bonded interactions to represent the fibers and employs a double-well potential to describe the non-bonded interactions within the matrix. Using this model, we investigate the fracture mechanics properties of Bouligand structures, with a particular focus on the emergence of helicoidal cracks. Our primary objective is to validate the hypothesis that these twisting cracks, which align with the fiber orientation, contribute to local hardening mechanisms. By hindering the growth of individual cracks, these hardening mechanisms facilitate the nucleation and growth of multiple cracks, thereby promoting a delocalization effect within the material. Through extensive simulations and analysis, we confirm the validity of our hypothesis. The presence of twisting cracks indeed induces local hardening mechanisms, making it more challenging for individual cracks to propagate. This phenomenon effectively spreads the damage, dissipating energy across larger volumes of the material. Consequently, the toughness of these Bouligand structures is enhanced, as this delocalization effect effectively mitigates the concentration of damage. These findings provide valuable insights into the fracture behavior of Bouligand structures and shed light into the underlying mechanisms responsible for their exceptional mechanical performance. Moreover, our CG model offers a practical and efficient approach to studying and understanding the fracture mechanics properties of complex fiber-reinforced composites. The ability to simulate and analyze the behavior of helicoidal cracks within Bouligand structures opens up new avenues for designing and optimizing advanced materials with enhanced toughness and damage resistance.

We explore the snap-through instability in hyper-elastic cylindrical tubes during inflation, specifically investigating the influences of geometry and imposed axial tensile loads on both the bulging shape profiles and the initiation pressure of the bulge. We perform bulging experiments on latex rubber tubes with different parameters such as the length-to-diameter aspect ratio and axial tension. To complement these experiments, finite element simulations across various geometries and a theoretical analysis of an infinite-length tube are conducted. Our simulations reveal a critical aspect ratio that divides the bulging into two possibilities: short tubes exhibit whole bulging, while longer tubes show localized bulging. Both experimental and simulation findings indicate that as the aspect ratio and axial tensile load increase, the initiation pressure diminishes and then converges. Notably, when the axial tensile load surpasses the shear modulus, it obstructs snap-through in shorter tubes and neutralizes the influence of the aspect ratio on the initiation pressure. The outcomes of this research offer valuable perspectives on modulating the bulging mode and initiation pressure in tubular structures within soft devices, including soft pneumatic actuators and energy harvesters.

This paper presents the results of an interfacial fracture study of Perovskite Light Emitting Devices (PLEDs). The interfacial robustness of the interfaces between the active layer and the adjacent layers of PLEDs is explored in an effort to simulate the effects of applied loads on pre-existing defects that are present in PLEDs. The dependence of interfacial fracture toughness on mode mixity (ratio of mode I and mode II) was studied using Brazil disk testing. The crack microstructure interactions associated with crack growth were then studied along with the underlying fracture modes and toughening mechanisms. The underlying toughening mechanisms were then modeled before discussing the implications of the current work for the design of mechanically robust PLEDs.

Although bending a sheet of paper is an easy operation, stretching is more limited and it leads to rupture and tears. However, well-designed cuts on the sheet can induce a large effective stretchability. This kirigami technique offers a large scope of engineering applications ranging from deployable structures to compliant electronics. We are here interested in the axisymmetric configuration where cuts are designed along concentric circles. Applying an increasing transverse load at the center of the sheet results into a 3D axisymmetric structure of growing amplitude which eventually saturates. We first describe the linear response of the structure and determine the evolution of the deployed shape until its asymptotic geometrical limit. Reversing the problem in the linear regime, we propose, a design procedure for the cuts leading to a desired 3D shape. The structure can also be deployed by inflating an inner balloon. Exploring further the interplay between mechanics and geometry, we finally describe the maximum volume of inflated kirigami structures as a function of the cutting pattern.

Clutch mechanisms that are lightweight and have low power consumption are crucial to enhancing the functionality of many robotic systems. We present the design, characterization, and modeling of a switchable clutch based on the self-amplified friction between thin flexible sheets. The clutch consists of interleaved paper sheets, which maintaining a locked state akin to that of interleaved books. Unlocking is achieved by applying rotation to the distal ends of the interleaved assembly, counteracting the self-amplified normal force within the layers. A 60-layer paper-based interleaved clutch, weighing 9 g, exhibits a locking force capacity of ∼550 N, along with an unlocked state force of ∼1 N. Through the incorporation of rubber bands, the clutch achieves bistable switching and self-resetting capabilities. In addition, we demonstrate an application of the clutch by integrating it into a wearable posture corrector.

The use of machine learning techniques to homogenize the effective behavior of arbitrary microstructures has been shown to be not only efficient but also accurate. In a recent work, we demonstrated how to combine state-of-the-art micromechanical modeling and advanced machine learning techniques to homogenize complex microstructures exhibiting non-linear and history dependent behaviors (Logarzo et al., 2021). The resulting homogenized model, termed smart constitutive law (SCL), enables the adoption of microstructurally informed constitutive laws into finite element solvers at a fraction of the computational cost required by traditional concurrent multiscale approaches. In this work, the capabilities of SCLs are expanded via the introduction of a novel methodology that enforces material symmetries at the neuron level, applicable across various neural network architectures. This approach utilizes tensor-based features in neural networks, facilitating the concise and accurate representation of symmetry-preserving operations, and is general enough to be extend to problems beyond constitutive modeling. Details on the construction of these tensor-based neural networks and their application in learning constitutive laws are presented for both elastic and inelastic materials. The superiority of this approach over traditional neural networks is demonstrated in scenarios with limited data and strong symmetries, through comprehensive testing on various materials, including isotropic neo-Hookean materials and tensegrity lattice metamaterials. This work is concluded by a discussion on the potential of this methodology to discover symmetry bases in materials and by an outline of future research directions.

We introduce a novel system of high-order wavelet collocation upwind schemes utilizing a broad class of second-generation wavelets for hyperbolic conservation laws. These schemes possess multiresolution self-adaptive capabilities and are integrated into a meshfree framework. Unlike traditional high-order schemes that necessitate frequent variable transformations between physical and characteristic spaces involving numerous local projections, our schemes enable the direct discretization of relevant spatial derivatives in the physical space. The newly proposed schemes make the most use of wavelet properties, i.e., applying asymmetrical interpolating wavelets to achieve the upwind property and utilizing the symmetrical second-generation wavelets to preserve shapes of functions and obtain higher data compression rates. Numerical tests of which solutions contain strong discontinuities and different scale smooth structures are conducted to demonstrate the enhanced performance of the proposed schemes based on the second-generation wavelets. The proposed schemes can utilize only 9 % of the nodes in the WENO5 scheme to yield similar results when addressing the advection of a square wave and achieve the convergent solution for the two interacting blast waves problem without local characteristics projection. In comparison to conventional methods and wavelet upwind schemes based on the interpolating wavelets we previously introduced, the newly proposed schemes exhibit a significantly higher data compression rate and yield substantial computational savings without compromising accuracy.

Gradient-structured metals have attracted a lot of attention due to their good synergy between strength and ductility. However, whether they can be used as a candidate for effective prevention of shear localization failure during high strain rate deformations is still an open question. Corresponding to the particular mechanisms of dynamic recrystallization and twinning at high strain rates, a physically based constitutive model of crystal plasticity is developed, including a new evolution equation for dislocation density and a twinning model, to investigate the detailed process of shear localization of gradient-structured CoCrFeMnNi high-entropy alloys (HEAs). A physically based strain gradient theory is considered to capture the strengthening effect of gradient structures. The competition between microstructural softening and strengthening effects is quantified to reveal the effects of gradient-structure on shear localization and shear localization can be significantly delayed in gradient-structured HEAs. This study contributes to the understanding of the influence of gradient-structure on shear localization and provides insights for further optimization of the mechanical behavior of gradient-structures at high strain rates to develop strong and ductile metals and alloys for dynamic applications.

The detrimental effect of ratchetting on the fatigue life of materials requires precise prediction models to guarantee the safety of engineering structures. This study focuses on predicting the uniaxial ratchetting of extruded AZ31 magnesium (Mg) alloy using machine learning (ML) based approaches. At first, the evolution and deformation mechanisms of ratchetting are summarized based on the existing experimental results of the Mg alloy. Subsequently, a semi-empirical prediction model, tailored for engineering applications, is developed to describe the evolution of ratchetting strain. Then, a pure data-driven ML based prediction model is proposed to overcome the shortcoming existed in the semi-empirical model and improve the prediction accuracy to the uniaxial ratchetting of the Mg alloy. Finally, a physics-informed ML based model, incorporating the physical information derived from the semi-empirical one, is proposed to further enhance its prediction accuracy and generalization ability. The comparison with correspondent experimental data demonstrates that the proposed physics-informed ML based model exhibits high prediction accuracy and generalization ability.