Optimization of 3D lattice cores in composite sandwich structures
Bibliographic record
Abstract
Hybrid lattice cores for sandwich structures containing both solid struts and polymer foam are a recent option available to designers. These cores benefit from a synergistic effect in which the foam supports the slender struts against buckling in addition to carrying a portion of the applied loads directly. This work will optimize these hybrid cores to minimize unit cell density given compressive and shear strength constraints for a variety of unit cell configurations. An analytical model is developed for prediction of failure using the assumption that the foam is an elastic foundation that supports the struts; however, the unit cells are found to be optimal when no polymer foam is used. With the problem thereby simplified, analytical optimization solutions are derived and studied extensively, revealing qualitative insights about efficient lattice core designs. Optimal strut inclination angle, failure mode, and configuration are plotted against loading, which show that, for the large majority of strength constraints, the lowest unit cell density is achieved with tetrahedral cores whose struts fail in compressive yielding.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".