Energy Absorption Properties of Periodic and Stochastic 3D Lattice Materials

Jochen Mueller, Kathryn H. Matlack, Kristina Shea, Chiara Daraio

Research output: Contribution to journalArticlepeer-review

Abstract

Architected lattices can be designed to have tailorable functionalities by controlling their constitutive elements. However, little work has been devoted to comparing energy absorption properties in different periodic three-dimensional geometries to each other and to comparable foam-like random structures. This knowledge is essential for the entire design process. In this work, the authors conduct a systematic and comprehensive computational study of the quasi-static and dynamic energy absorption properties of various different geometries. They test compression loading over strain rates varying from 1 to 104 s−1. The authors analyze geometries with varying degrees of nodal connectivity, ranging from bending dominated to stretching dominated, at different orientations, and compare their response to equivalent stochastic lattices. Results show relatively high stress peaks in the periodic lattices, even in bending dominated lattices at certain orientations. Conversely, the stochastic geometries show a relatively constant stress response over large strains, which is ideal for energy absorbing applications. Still, results show that specific orientations of bending dominated periodic lattice geometries outperform their stochastic equivalents. This work can help to quickly identify the potential of different unit cell types and aid in the development of lattices for impulse mitigation applications, such as in protective sports equipment, automotive crashworthiness, and packaging.

Original languageEnglish (US)
Article number1900081
JournalAdvanced Theory and Simulations
Volume2
Issue number10
DOIs
StatePublished - Oct 1 2019

Keywords

  • architected structures
  • cellular solids
  • energy absorption
  • fracture
  • lattice structures

ASJC Scopus subject areas

  • General
  • Modeling and Simulation
  • Numerical Analysis
  • Statistics and Probability

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