Energy absorption of central self-similar honeycombs under quasi-static axial load

材料科学 蜂巢 结构工程 复合材料 准静态过程 吸收(声学) 能量(信号处理) 物理 工程类 热力学 量子力学
作者
Chenghao Guo,Xueyu Cheng,Lixin Lu,Liao Pan,Jun Wang
出处
期刊:International Journal of Mechanical Sciences [Elsevier]
卷期号:274: 109264-109264 被引量:25
标识
DOI:10.1016/j.ijmecsci.2024.109264
摘要

At present, improving the energy absorption capacity of lightweight degradable polymer honeycombs is crucial for one of the future engineering applications and bio-inspired strategy based on hierarchy is considered an efficient method for designing honeycomb structures. To improve the energy absorption performance and stability of polymer honeycombs, this study develops three central self-similar bio-inspired honeycombs combining the microstructure of horsetails and designs different connections between the two walls and connecting structures for the central self-similar honeycombs. Bio-inspired honeycombs are fabricated using toughened polylactic acid (PolyMaxTM PLA) through fused deposition modelling and subjected to axial compression tests. The finite element (FE) models of the bio-inspired honeycombs are developed to simulate the axial compression process and verified with experimental results. Results show that the connecting structures distributed on the outer wall of the central self-similar honeycombs considerably improve the energy absorption performance of the bio-inspired honeycombs. In the double-cell honeycombs, connecting structures interact with the cell walls, leading to the formation of more folding lobes on the cell walls and increased cell wall utilisation. Furthermore, the effects of geometrical parameters on the energy absorption performance of the bio-inspired honeycombs are investigated. Suitable connecting structure size and cell wall thickness can improve the energy absorption performance and stability of the honeycombs. In addition, the theoretical calculation models of the three bio-inspired honeycombs are established, and the errors with the FE calculation results are within 6%, which verifies the accuracy of the theoretical models.
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