Effect of fillers on compression loading performance of modified re-entrant honeycomb auxetic sandwich structures
Bibliographic record
Abstract
The auxetic sandwich panels for structures have been designed to provide impact protection. The aim of this work is to modify an auxetic (re-entrant) honeycomb cell to reduce the stress concentrations within the cell structure, and further enhancement of this design. The auxetic structure was filled to achieve a greater energy absorbance and enhance safety applications. Analytical and elastic three-dimensional finite element approaches were used to investigate the structural strength performance. The basic model (i.e. modified re-entrant strut cell design) consisted of the honeycomb auxetic polypropylene (PP) structure sandwiched between two steel plates (known as safety panels) which were placed under static compression loading. The cell geometry and size were then modified to reduce the stress concentration zones. The structure cells were filled with silly putty and polyvinyl chloride (PVC) foam. The effect of the filling the cells on the stress concentration and energy absorbance were analysed using elastic stress and deformation analysis methods. During the stress path analysis, it was found that an increase in Young’s modulus of the filling was directly proportional to a decrease in internal stresses. It was concluded that while filling the basic model with soft materials reduced the stress concentration, but it led to a reduction in the energy absorbance capability. Further on, the lower stress produced by the enhanced could be useful to prevent significant penetration of the protective panel. Compared to similar structures of steel, auxetic foam panels have the advantage of having only a fraction of the weight and being corrosion resistant at the same time as keeping impact strength.Graphical abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".