The Energy Absorption Behavior of 3D-Printed Polymeric Octet-Truss Lattice Structures of Varying Strut Length and Radius
Bibliographic record
Abstract
We investigate the compressive energy absorption performance of polymeric octet-truss lattice structures that are 3D printed using high-resolution stereolithography. These structures are potential candidates for personal protective equipment, structural, and automotive applications. Two polymeric resins (high-strength/low-ductility and moderate-strength/high-ductility) were used in this work, and a comprehensive uniaxial tensile characterization was conducted to establish an optimal UV curing time. The external octet-truss structure geometry (3″ × 3″ × 3″) was maintained, and four different lattice cell densities (strut length, L) and three different strut radii (R) were printed, UV cured, and compression tested. The compressive stress–strain and energy absorption (EA) behavior were quantified, and the EA at 0.5 strain for the least dense and smallest R structure was 0.02 MJ/m3, while the highest density structure with the largest R was 1.80 MJ/m3 for Resin 2. The structural failure modes varied drastically based on resin type, and it was shown that EA and deformation behavior were related to L, R, and the structures’ relative density (ρ¯). For the ductile resin, an empirical model was developed to predict the EA vs. compressive strain curves based on L and R. This model can be used to design an octet-truss lattice structure based on the EA requirements of an application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".