3D printed octet plate-lattices for tunable energy absorption
Bibliographic record
Abstract
Tunable energy absorption achieved through grading of lattice structures shows high potential to be used in lightweight cellular cores for energy absorbing structures. This study investigates structurally graded and multi-material lattices consisting of plate-based octet unit cells, under quasi-static compression, to assess their energy absorption ability. Variations in the structure and material compositions of the plate-lattice structures are achieved through changing the plate thickness and through changing the filament material along the lattice in the direction of applied compressive force. The compressive stress–strain behavior reveals a near 10% increase of specific energy absorption (SEA) in the plate thickness graded designs at higher strain compared to the baseline octet lattices. The multi-material arrangements significantly modified onset location of the structure collapse. Finite element models of the structures were developed, and good agreements with experimental results were observed. Effects of varying each of the unit cell geometric parameters were analyzed, and the high sensitivity to the plate inclination angle, which resulted in greater changes to the maximum stress values and control of the overall SEA, was identified. The results demonstrate the capacity to adapt the octet lattice structure design through additive manufacturing to better suit the expected load and 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) | 0.001 | 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".