Virtual-to-physical design of functionally graded mechanical metamaterials for buckling-critical performance

  • Azher, Kashif (King Fahd University of Petroleum & Mineral)
  • Nazir, Aamer (King Fahd University of Petroleum & Mineral)

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Localised buckling often caps the performance of architected lattices at fixed mass [1], [2]. We address this limit by designing mass-conserving, functionally graded (FG) octet mechanical metamaterials that reallocate strut cross-sections and introduce vertical load paths while preserving relative density and the bounding box. The workflow couples computation and experiment: lattices are modelled in ANSYS with large-deformation contact and a multilinear isotropic hardening law, capturing elastic-plastic behaviour, and are validated against quasi-static compression tests to 65% strain. We report compression modulus (CM), peak load, energy absorption (EA), mean crushing force (MCF), and crushing force efficiency (CFE), and benchmark results in Gibson–Ashby density-normalised space to separate architectural effects from density [3], [4]. At the same relative density, the best FG design attains an increase in CM, peak load, EA, and MCF versus a uniform octet. Simulations reproduce the measured force-displacement trajectories and collapse morphologies and explain the mechanism: parallel axial paths and increased nodal restraint reduce effective slenderness, delay the first instability, and lift the sustained plateau load – consistent with stretch-dominated scaling for the octet in the Gibson-Ashby framework [5]. CFE shows the expected peak-to-plateau trade-off: efficiency may fall when the initial peak rises faster than the plateau, even as absolute crash performance (MCF, EA) improves. Benchmarking against published polymer and metallic lattices underscores that careful local material redistribution can close part of the gap to metal references without adding mass. The approach is topologyagnostic (transferable to other plate-based and TPMS mechanical metamaterials) and amenable to surrogate/ML-guided exploration of larger design spaces with manufacturability constraints.