Fracture toughness evaluation of zeolite/polyurethane-filled woven panels
Bibliographic record
Abstract
Recently, due to their extraordinary strength-to-weight ratio and multi-functional applications, three-dimensional woven fiberglass sandwich structures have become a well-received topic by researchers and manufacturers. Nevertheless, using light and foam absorber materials as injected fillers within sandwich cores can improve their overall mechanical performance and, in particular, their fracture toughness behavior. This study evaluates the fracture toughness of three-dimensional woven fiberglass sandwich panels filled with natural nano-structured zeolite/polyurethane foams injected between their parallel panels. The Single-Edge Notched Bend test was carried out to understand the effect of the injected foam on the mode-I fracture toughness response. It is demonstrated that the polyurethane foam reinforced with natural nano-structured zeolite particles highly improved the fracture toughness of sandwich core panels. It was found that the presence of vertical glass yarns within the sandwich panel gallery resulted in a significantly higher toughness compared with typical sandwich panels of no reinforcing vertical columns confirmed by the crack propagation and observed failure mode. The SEM and EDX analyses were used to better understand the correlations amongst the specimen morphology, the cracks behavior, and the toughness exhibited by the fabricated specimens.
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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".