Predicting the influence of geometric imperfections on the mechanical response of 2D and 3D periodic trusses
Predicting the influence of geometric imperfections on the mechanical response of 2D and 3D periodic trusses
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DOI：
10.1016/j.actamat.2023.118918
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发表时间：
2023-05-20
期刊：
影响因子：
9.4
通讯作者：
Kochmann, D. M.
中科院分区：
文献类型：
--
作者：
Glaesener, R. N.;Kumar, S.;Kochmann, D. M.
虽然基于桁架网络的建筑材料已被证明具有有利或极端的机械性能，但这些性能可能会受到制造过程中的公差和不确定性的高度影响，而这些公差和不确定性通常在设计阶段被忽略。确定性的计算工具通常设计的结构与完美的，无缺陷的架构的假设，而实验已经证实了不可避免的存在的缺陷和它们可能对有效性能的不利影响。关于从增材制造过程中出现的几何缺陷的性质和预期大小的信息将允许旨在减轻（或至少考虑）缺陷的影响并减少有效特性的不确定性的新设计。为此，我们在这里调查桁架中最常见的四种几何缺陷类型的影响，适用于11个代表性的桁架拓扑结构在二维和三维。通过我们的研究，我们（i）通过计算均匀化量化缺陷对有效刚度的影响，（ii）检查各种桁架拓扑结构对这些缺陷的敏感性，（iii）通过3D打印桁架的实验证明模型的适用性，以及（iv）提出机器学习框架以仅基于其机械响应来预测给定桁架架构中缺陷的存在。
Although architected materials based on truss networks have been shown to possess advantageous or extreme mechanical properties, those can be highly affected by tolerances and uncertainties in the manufacturing process, which are usually neglected during the design phase. Deterministic computational tools typically design structures with the assumption of perfect, defect-free architectures, while experiments have confirmed the inevitable presence of imperfections and their possibly detrimental impact on the effective properties. Information about the nature and expected magnitude of geometric defects that emerge from the additive manufacturing processes would allow for new designs that aim to mitigate (or at least account for) the effects of defects and to reduce the uncertainty in the effective properties. To this end, we here investigate the effects of four most commonly found types of geometric imperfections in trusses, applied to eleven representative truss topologies in two and three dimensions. Through our study, we (i) quantify the impact of imperfections on the effective stiffness through computational homogenization, (ii) examine the sensitivity of the various truss topologies with respect to those imperfections, (iii) demonstrate the applicability of the model through experiments on 3D-printed trusses, and (iv) present a machine learning framework to predict the presence of defects in a given truss architecture based merely on its mechanical response.