Development of Numerical Model for the Crashworthiness of Additively Manufactured Sandwich Lattices
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2023-2200.vid Compared to other materials, cellular solids have superior energy absorption capabilities. Of particular interest within this material category are periodic lattice materials, which – in combination with advances in additive manufacturing technologies – allow not only for repeatable behavior, but also for a high degree of customization. In this paper, the crashworthiness of “sandwich” lattice structures is investigated, using both experimental and numerical investigations. After characterizing the quasi-static mechanical performance of solid nylon-carbon fiber and a solid engineering resin material, the response of single-layer cubic and octet lattices with a relative density of 30% made from those materials was characterized and compared. The response of multi-layer cubic and octet lattices was investigated before finally layering single-layer octet and cubic topologies to form two unique “sandwich” lattices. Stress-strain, efficiency-strain and other crashworthiness parameter data was gathered, and it was found that while the three-layer single-topology lattices were capable of absorbing 9.8 J (cube) and 7.8 J (octet), the designed sandwich lattices were experimentally capable of absorbing more: 19.0 J (octet-cube-octet) and 22.4 J (cube-octet-cube).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".