Compression and bending performance of thermoplastic corrugated sandwich panels with recycled PET foam
Bibliographic record
Abstract
The core topology of sandwich panels plays a pivotal role in determining their mechanical performance, weight efficiency, and multifunctional capabilities. This paper experimentally and numerically investigates the mechanical performance of corrugated core sandwich panels with varied core geometries, including triangular, trapezoidal, rectangular, and circular, under compression and bending loads. Panels were fabricated using polylactic acid (PLA) and tested both with and without the addition of recycled polyethylene terephthalate (R-PET) foam inserts. R-PET foam, a sustainable material, offers benefits in strength-to-weight ratio, moisture, thermal, and acoustic insulation. The results indicate that core geometry significantly impacts load-bearing capacity, with rectangular cores exhibiting superior compression strength while triangular cores excel under bending loads. The foam inserts, placed within the core channels, notably improved compressive and flexural load capacity by, on average, 270 and 220% across all geometries with only a 30% increase in weight. Failure modes were also observed, with initial mode 1 buckling of the core walls and localized deformation leading to subsequent failure mechanisms such as delamination and core debonding. ABAQUS was used to develop finite element models for flatwise compression and three-point bending tests for all core geometries. The numerical simulations closely aligned with the experimental results, providing insights into stress distribution, the influence of cell wall thickness, and the impact of corrugation angles on panel performance. These findings highlight the critical role of core geometry and the impact of foam inserts in enhancing structural integrity in sandwich panels.
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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".