Additive manufacture of ultrasoft bioinspired metamaterials

超材料 动载荷 动态范围压缩 航空航天 材料科学 设计工具 智能材料 机械工程 计算机科学 纳米技术 工程类 航空航天工程 复合材料 光电子学
作者
Zhenyang Gao,Pengyuan Ren,Hongze Wang,Zijue Tang,Yi Wu,Haowei Wang
出处
期刊:International Journal of Machine Tools & Manufacture [Elsevier]
卷期号:195: 104101-104101 被引量:9
标识
DOI:10.1016/j.ijmachtools.2023.104101
摘要

The dynamic loading behavior of materials plays a vital role in various engineering applications, such as aerospace protective components, armor, marine infrastructures, and automotive crash safety. The advent of additive manufacturing technologies has enabled the design of metamaterials that exhibit exceptional mechanical performance and artificially engineered properties not found in nature. However, fabricating ideal materials that resist dynamic loading is challenging because of the complexity of dynamic mechanical processes and varying requirements across different applications. In this study, a novel hierarchical design is proposed that combines natural fiber-inspired frameworks with graphene-inspired parent structures. This design aims to produce metamaterials, with characteristics such as reduced dynamic compressive strength, high energy absorption, and programmable dynamic loading, via advanced manufacturing technologies. An additive-manufacturing-oriented digital design approach and machine learning techniques were employed to engineer the dynamic loading performance of graphene-inspired metamaterials using the bonding principles inspired by natural fibers, to facilitate the design of next-generation metamaterial for advanced manufacturing. Experimental results illustrate the significant improvements achieved with our metamaterials compared to their existing counterparts. These improvements include a decrease in dynamic compressive strength of up to 86 %, while maintaining a remarkable 682 % enhancement in energy absorption during dynamic compressions, with a 42 % reduction in the energy decay rate. A compositional design strategy and programmable dynamic compression curve methodology is proposed that enable the tailored optimization of dynamic loading behaviors without modifying the base topology of metamaterials. This research offers a promising pathway for the development of next-generation materials, engineered to withstand dynamic loadings with intelligent and programmable performances suitable for aerospace, defense, and other high-value applications. By leveraging the advantages of natural fiber-inspired structures and graphene-inspired metamaterials, this work contributes to the advancement of materials with tailored resistance to dynamic loading and opens new possibilities for intelligent dynamic loading performance.
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