Purpose This paper aims to evaluate and optimize the compressive energy absorption behavior of two architected polymer lattice systems – a conventional body-centered cubic (BCC) strut-based structure and a newly proposed hybrid triply periodic minimal surface (TPMS) design (Neosch, NS) – fabricated via fused deposition modeling. This study focuses on identifying geometrical parameters that maximize energy absorption under different strain rates. Design/methodology/approach This research integrates experimental compression tests, finite element simulations and statistical optimization. A central composite design under response surface methodology was used to explore the effects of wall thickness, strut spacing and aspect ratio. Validation was achieved through close matching between simulated and experimental stress–strain data, supported by Gibson–Ashby scaling to interpret deformation mechanisms. Findings Results revealed that the hybrid NS lattice exhibited significantly higher energy absorption and strain-rate sensitivity than the BCC structure, attributed to its continuous, joint-free topology that promotes stable collapse. Optimal energy absorption was found near a wall thickness of 1.2 mm and strut spacing of 3.5 mm. The transition from bending- to stretch-dominated behavior with increasing relative density was confirmed, enhancing load-bearing efficiency. Research limitations/implications This study used quasi-static simulations with simplified isotropic material behavior, which may not fully capture dynamic effects. Future work should include viscoelastic and strain-rate-dependent models and explore fatigue performance. Practical implications Findings support the design of lightweight, energy-absorbing structures for impact-critical applications such as automotive crash components, aerospace protective panels and biomedical implants, offering tailored solutions by adjusting key geometric parameters. Originality/value This work introduces a novel hybrid TPMS geometry and systematically optimizes its performance, demonstrating how integrating experimental validation, simulations and statistical tools can yield predictive frameworks for architected materials under compressive loading.
Ansari et al. (2026) studied this question.