今日更新:International Journal of Solids and Structures 1 篇,Mechanics of Materials 1 篇,Thin-Walled Structures 1 篇
Ultra-long-range force transmission in fiber networks enabled by multiaxial mechanical coupling
Jingnan Liu, Mengyuan Wang, Chao Xue, Hongfa Wang, Hailong Wang
doi:10.1016/j.ijsolstr.2024.112698
利用多轴机械耦合实现光纤网络中的超远距离力传输
Force transmission in the extracellular matrix is crucial for cellular mechanosensing. This transmission is influenced by factors such as tension–compression asymmetric stiffness and the fiber alignment of fibrous materials. However, the role of the anomalous Poisson’s ratio, intrinsic to fibrous materials, in force transmission remains underexplored. In this study, we utilize discrete fiber network simulations with different levels of connectivity to examine the stress decay of cell contraction in fibrous matrices. Our findings show that highly connected fiber networks exhibit reduced fiber alignment and atypical tensile hoop stress. This leads to an ultra-slow decay of radial stress induced by isotropic contraction of spherical cells. Delving deeper, we discovered that the increase of network connectivity corresponded to an enhanced Poisson’s ratio, signifying a pronounced multiaxial coupling effect. To fully comprehend this multiaxial coupling, we develop a constitutive law for fibrous materials. This law considers the stiffening along the tensile direction and their significant transverse contraction. Theoretical analysis elucidates that the stress decay of cell contraction adheres to a scaling law, represented as σrr-n, with the decay exponent n ranging from 1.5 to 3. Notably, this finding diverges from prior predictions that n is more than 2. The combination of a high tension-to-compression stiffness ratio with strong multiaxial coupling leads to ultra-long-range force transmission in fibrous materials. This ultra-long-range force transmission is marked by a convergent diminishing n approximating 1.5. In summary, our study provides a quantitative framework for elucidating the maximum limit of the force transmission range and serves as a guideline for developing innovative biomimetic materials.
细胞外基质中的力传递对于细胞机械传感至关重要。这种传递受到纤维材料的拉伸-压缩不对称刚度和纤维排列等因素的影响。然而,纤维材料固有的反常泊松比在力传递中的作用仍未得到充分探索。在本研究中,我们利用不同连通性水平的离散纤维网络模拟来研究纤维基质中细胞收缩的应力衰减。我们的研究结果表明,高度连通的纤维网络表现出纤维排列减少和非典型拉伸箍应力。这导致球形细胞各向同性收缩引起的径向应力超慢衰减。深入研究后,我们发现网络连通性的增加与泊松比的提高相对应,这表明存在明显的多轴耦合效应。为了充分理解这种多轴耦合,我们开发了纤维材料的构成定律。该定律考虑了纤维材料沿拉伸方向的刚度及其显著的横向收缩。理论分析表明,细胞收缩的应力衰减遵循σrr-n 的缩放定律,衰减指数 n 在 1.5 到 3 之间。高拉伸-压缩刚度比与强多轴耦合的结合导致了纤维材料中的超远距离力传递。这种超长距离力传递的特点是 n 趋近于 1.5 的收敛递减。总之,我们的研究为阐明力传递范围的最大极限提供了一个定量框架,可作为开发创新型仿生材料的指南。
An end-to-end explainable graph neural networks-based composition to mechanical properties prediction framework for bulk metallic glasses
Tao Long, Zhilin Long, Bo Pang
doi:10.1016/j.mechmat.2024.104945
基于可解释图神经网络的块状金属玻璃从成分到机械性能的端到端预测框架
Accurate prediction of the properties of bulk metallic glasses (BMGs) can provide an important guideline for the design of novel BMGs. While various machine learning (ML) models have been employed to predict the properties of BMGs, feature engineering is typically necessary to choose suitable descriptors based on domain knowledge or experience. In this work, an end-to-end generic framework has been proposed based on graph neural networks (GNNs) for composition-to-property prediction of BMGs. Firstly, an innovative graph representation of alloy compositions is designed. Then, two classes of GNNs have been developed to predict the fracture strength and plastic strain of BMGs. The R2 values for the optimal model on the test set were 0.963 and 0.801, respectively. Additionally, the optimal model has been fine-tuned using transfer learning for the problem of skewed distributions in the plastic strain dataset. As a result, the R2 scores on the test set improved significantly by 23.8% and 6.24%, respectively. Finally, a Hybrid Explainer has been developed to explain the entire prediction process of the model. The results of this study indicate that the proposed GNNs models may be informative for the rational design of BMGs.
准确预测块状金属玻璃(BMGs)的特性可为新型 BMGs 的设计提供重要指导。虽然各种机器学习(ML)模型已被用于预测块状金属玻璃的特性,但特征工程通常需要根据领域知识或经验选择合适的描述符。在这项工作中,我们提出了一个基于图神经网络(GNN)的端到端通用框架,用于从成分到属性预测 BMG。首先,设计了一种创新的合金成分图表示法。然后,开发了两类图神经网络来预测 BMG 的断裂强度和塑性应变。测试集上最优模型的 R2 值分别为 0.963 和 0.801。此外,针对塑性应变数据集中的偏斜分布问题,利用迁移学习对最优模型进行了微调。结果,测试集上的 R2 分数分别显著提高了 23.8% 和 6.24%。最后,还开发了一个混合解释器来解释模型的整个预测过程。本研究的结果表明,所提出的 GNNs 模型可能对合理设计 BMG 具有参考价值。
Buckling and post buckling analysis of spatial thin film structures under shearing based on perturbation method
Meng Li, Bo-Hua Sun
doi:10.1016/j.tws.2024.111679
基于扰动法的剪切作用下空间薄膜结构的屈曲和后屈曲分析
Spatial thin film structures exhibit high sensitivity to shear deformation, often exhibiting wrinkling phenomena under minimal shear loads. In this study, we used a combined experimental and theoretical approach to investigate the wrinkling and post-buckling behaviors of thin films subjected to shear forces. Initially, we designed and fabricated a high-precision experimental apparatus, which, in conjunction with the MTS Exceed 40 series universal testing machine, was a versatile experimental platform for evaluating the responses of thin films to shear. Subsequently, we extended the classical Föppl–von Kármán nonlinear plate model to capture the large deformation behaviors of thin films under shear. This development led to the formulation of governing equations that describe the shear-induced deformation of the films. We introduced two boundary conditions to characterize these deformations: one in which the non-loaded edge was free (unconstrained), and another in which the non-loaded edge was constrained (analogous to a frame constraining a painting). To solve the governing equations, we treated the incremental shear displacement angle of the thin film as a small parameter and devised a numerical method grounded in perturbation theory. We further formulated an implicit difference method of arbitrary order accuracy to enhance the precision and stability of the numerical solution. Our experimental and theoretical analyses revealed a comprehensive buckling pathway for thin films under shear, characterized by an initial wrinkling configuration (Config. 1), followed by a transition to a secondary wrinkling state (Config. 2), and culminating in a wrinkling splitting pattern (Config. 3). We also determined that by increasing the pre-stretching of thin films, their resistance to wrinkling along the entire buckling pathway could be significantly enhanced.
空间薄膜结构对剪切变形具有高度敏感性,通常在最小剪切负荷下就会出现起皱现象。在本研究中,我们采用实验和理论相结合的方法来研究薄膜在剪切力作用下的起皱和后屈曲行为。首先,我们设计并制造了一台高精度实验仪器,该仪器与 MTS Exceed 40 系列万能试验机配合使用,是评估薄膜剪切响应的多功能实验平台。随后,我们扩展了经典的 Föppl-von Kármán 非线性板模型,以捕捉薄膜在剪切作用下的大变形行为。这一发展促成了描述薄膜剪切变形的控制方程的形成。我们引入了两种边界条件来描述这些变形:一种是非负载边缘是自由的(无约束),另一种是非负载边缘是受约束的(类似于画框对绘画的约束)。为了求解控制方程,我们将薄膜的增量剪切位移角视为一个小参数,并设计了一种基于扰动理论的数值方法。我们进一步制定了一种任意阶精度的隐式差分法,以提高数值求解的精度和稳定性。我们的实验和理论分析揭示了薄膜在剪切力作用下的综合屈曲途径,其特点是初始起皱构型(构型 1),随后过渡到二次起皱状态(构型 2),并最终形成起皱分裂模式(构型 3)。我们还确定,通过增加薄膜的预拉伸,可以显著增强薄膜在整个弯曲路径上的抗皱性。