Machine learning assisted design and optimization of plate-lattice structures with superior specific recovery force

IF 5.4 1区化学 Q2 CHEMISTRY, MULTIDISCIPLINARY

GIANT Pub Date : 2024-05-09 DOI:10.1016/j.giant.2024.100282

Amir Teimouri, Adithya Challapalli, John Konlan, Guoqiang Li

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Abstract

In load carrying structures and devices, there is a growing need for shape memory polymer (SMP) metamaterials that are lightweight and have superior strength, remarkable flexibility, and substantial specific recovery force (SFR). One of the challenges is to find optimum lightweight structures with high SFR. To address this challenge, we propose a novel inverse design framework to design plate-lattice structures (PLSs) with user-defined optimum specific maximum compression strength. Consisting of three sub-frameworks, the performance of the inverse design framework was validated before it was utilized to optimize PLSs. The optimum PLSs developed are fabricated with 3D printing using a novel SMP. In addition, we have printed a solid cylinder and Cubic+Octet (control) PLSs to compare their structural capacity with the predicted structures. The optimized PLSs display 30 ∼ 170 % greater SFR compared to the control PLS and solid cylinder. These findings suggest a promising strategy for enhancing the effectiveness of actuators based on SMP mechanical metamaterials. The inverse design framework has the potential to be utilized for generating structures with user-defined optimum mechanical properties.

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机器学习辅助设计和优化具有超强比恢复力的板格结构

在承载结构和设备中，对形状记忆聚合物（SMP）超材料的需求与日俱增，这些超材料重量轻、强度高、柔韧性好、比恢复力（SFR）大。其中一项挑战是找到具有高 SFR 的最佳轻质结构。为了应对这一挑战，我们提出了一种新颖的逆向设计框架，用于设计具有用户定义的最佳特定最大压缩强度的板格结构（PLS）。该反向设计框架由三个子框架组成，在用于优化 PLS 之前，对其性能进行了验证。开发出的最佳 PLS 使用新型 SMP 通过 3D 打印制造。此外，我们还打印了实心圆柱体和立方体+八面体（对照）PLS，以比较它们与预测结构的结构能力。与对照 PLS 和实心圆柱体相比，优化 PLS 的 SFR 高出 30 ∼ 170%。这些发现为提高基于 SMP 机械超材料的致动器的有效性提供了一种有前途的策略。反向设计框架可用于生成具有用户定义的最佳机械性能的结构。

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来源期刊

GIANT Multiple-

CiteScore

8.50

自引率

8.60%

发文量

审稿时长

42 days

期刊介绍： Giant is an interdisciplinary title focusing on fundamental and applied macromolecular science spanning all chemistry, physics, biology, and materials aspects of the field in the broadest sense. Key areas covered include macromolecular chemistry, supramolecular assembly, multiscale and multifunctional materials, organic-inorganic hybrid materials, biophysics, biomimetics and surface science. Core topics range from developments in synthesis, characterisation and assembly towards creating uniformly sized precision macromolecules with tailored properties, to the design and assembly of nanostructured materials in multiple dimensions, and further to the study of smart or living designer materials with tuneable multiscale properties.