论文标题

3D印刷多物质仿生复​​合材料的非线性粗粒模型

Nonlinear Coarse-graining Models for 3D Printed Multi-material Biomimetic Composites

论文作者

Saldivar, Mauricio Cruz, Doubrovski, Eugeni L., Mirzaali, Mohammad J., Zadpoor, Amir A.

论文摘要

生物启发的复合材料是模仿天然材料的非凡和高效特性的一个巨大希望。逐素体素3D打印的最新发展使得对材料沉积的极端控制水平,从而产生了复杂的微构造材料。但是,空间复杂性使找到硬和软相的最佳分布成为一个巨大的挑战。为了解决这个问题,开发了一种非线性粗粒剂方法,其中使用基于泡沫的本构方程来预测仿生复合材料的力学。通过将粗粒元素预测与使用数字图像相关测量的全场应变分布进行比较,可以通过比较粗粒的有限元预测来验证所提出的方法。为了评估模型准确性的粗粒度程度,对以二进制版本的著名绘画装饰的预注明标本进行了建模。随后,粗晶粒用于预测包含复杂设计的生物启发的复合材料(例如功能梯度和分层组织)的断裂行为。最后,作为提出方法的展示,将逆性粗粒与骨组织适应的理论模型结合在一起,以优化3D打印的股骨的微体系结构。预测的性质与相应的实验结果非常吻合。因此,粗粒方法允许设计具有可调和可预测性能的高级架构材料。

Bio-inspired composites are a great promise for mimicking the extraordinary and highly efficient properties of natural materials. Recent developments in voxel-by-voxel 3D printing have enabled extreme levels of control over the material deposition, yielding complex micro-architected materials. However, spatial complexity makes it a formidable challenge to find the optimal distribution of both hard and soft phases. To address this, a nonlinear coarse-graining approach is developed, where foam-based constitutive equations are used to predict the mechanics of biomimetic composites. The proposed approach is validated by comparing coarse-grained finite element predictions against full-field strain distributions measured using digital image correlation. To evaluate the degree of coarse-graining on model accuracy, pre-notched specimens decorated with a binarized version of a renowned painting were modeled. Subsequently, coarse-graining is used to predict the fracture behavior of bio-inspired composites incorporating complex designs, such as functional gradients and hierarchical organizations. Finally, as a showcase of the proposed approach, the inverse coarse-graining is combined with a theoretical model of bone tissue adaptation to optimize the microarchitecture of a 3D-printed femur. The predicted properties were in exceptionally good agreement with the corresponding experimental results. Therefore, the coarse-graining method allows the design of advanced architected materials with tunable and predictable properties.

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