A systematic understanding of the toughening and self-healing mechanisms of artificial soft tissues is crucial for advancing their robust application in biomedical engineering. However, current models predominantly possess a phenomenological nature, often devoid of micromechanical intricacies and quantitative correlation between microstructure damage and macroscopic energy dissipation. To bridge this gap, we propose a novel energy dissipation mechanism-motivated network model that incorporates three unique physical ingredients with sound theoretical basis for the first time. These innovated features include the bond percolation-mediated network density and stiffness, the damage-induced energy dissipation and stress softening, and the entropic elasticity for the highly stretchable second network. The validity of this model was examined by implementing it within a meshfree peridynamic framework for artificial soft tissues subjected to simple tension and pure shear tests. We quantitatively correlated the dissipation with the network damage to reveal the effects of network density, the breaking stretch dispersion and the stiffness ratio. Our findings highlighted that the inhomogeneity and dispersion of materials properties play significant roles in the controllable progressive damage and dissipation, thereby offering valuable guidance for designing tougher artificial soft tissues. By reactivating the failed network, we further successfully captured the self-healing behaviors of artificial soft tissues. Our work provides an inspiring modeling framework for studying toughening mechanisms of artificial soft tissues.

中文翻译：

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人工软组织中的损伤诱导能量耗散


系统地了解人工软组织的增韧和自愈机制，对于推进它们在生物医学工程中的稳健应用至关重要。然而，目前的模型主要具有现象学性质，通常缺乏微观力学的复杂性以及微观结构损伤和宏观能量耗散之间的定量相关性。为了弥合这一差距，我们提出了一种新的能量耗散机制驱动的网络模型，该模型首次结合了三种独特的物理成分，并具有坚实的理论基础。这些创新特征包括键渗流介导的网络密度和刚度、损伤诱导的能量耗散和应力软化，以及高度可拉伸的第二网络的熵弹性。通过在无网格的近场动力学框架中实施该模型来检查该模型的有效性，该框架用于经受简单拉伸和纯剪切测试的人造软组织。我们将耗散与网络损伤定量相关，以揭示网络密度、断裂拉伸分散和刚度比的影响。我们的研究结果强调，材料性能的不均匀性和分散性在可控的渐进损伤和耗散中起着重要作用，从而为设计更坚韧的人造软组织提供有价值的指导。通过重新激活失败的网络，我们进一步成功地捕捉了人工软组织的自我修复行为。我们的工作为研究人工软组织的增韧机制提供了一个鼓舞人心的建模框架。