4.7 Article

Energy absorption characteristics of square frustum lattice structure

期刊

COMPOSITE STRUCTURES
卷 275, 期 -, 页码 -

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.compstruct.2021.114492

关键词

Lattice structure; Energy absorption; Crashworthiness; Quasi-static axial crushing; Theoretical prediction

资金

  1. National Natural Science Foundation of China [51675537]
  2. Open Sharing Fund for the Large-scale Instruments and Equipments of Central South University

向作者/读者索取更多资源

The study designed a square frustum lattice structure (SFLS) using a stable flat-topped pyramid as the structural cell and analyzed its energy absorption characteristics through experiments and simulations. It was found that the parameterization of SFLS influenced its energy absorption performance, and the modified theoretical prediction model accurately predicted the structure's performance.
Lattice structure has become an important energy absorber with its light weight and high strength ratio. In this paper, the stable flat-topped pyramid is used as the structural cell, and square frustum lattice structure (SFLS) with excellent energy absorption performance is designed. The geometry of SFLS is controlled by three factors: cell height, side length ratio and cell number. The quasi-static compression experiment and the finite element (FE) simulation were used to analyse the energy absorption characteristics of SFLS. The results show that the deformation of SFLSs is tightened folding mode, and produce a quasi-rectangular energy absorption curve. The parameterization study found that the better energy absorption performance is the SFLS with arbitrary cell number value, small cell height value and side length ratio close to 1, and the maximum SEA value can reach 72.05 J/g. Finally, by modifying the theoretical prediction model of mean crushing force and comparing with the FE results, it is found that the introduced correction coefficient can reduce the absolute error value from 32.18% to 4.96%, which indicates that the modified theoretical prediction model can be accurately used for prediction.

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