Design of New Hybrid Composites using Metal Embedded in Polymer Foam and Foam Composite
Bibliographic record
Abstract
Adhesion and loading bearing properties of polyurethane (PU) foams and sandwich composite with metallic inserts are studied. Metal or solid polymer anchors are used as the load transfer components for PU foam and sandwich composites when they are used as the structural element in design. The traditional method of fixation of these components in foams is gluing and fastening. In this work, the anchors are in the form of inserts and are imbedded in the PU during the foaming process. Flexural testing was conducted on PU with and without metallic inserts to establish typical interaction trends. The load-deflection response, mode of failure, and fracture stresses of the PU structures are elucidated. Results show that long taper and leaf inserts imbedded in foam and sandwich composite provide better load carrying capacity. Comparisons between the taper and leaf inserts are documented. Leaf inserts inside a foam and sandwich composite show better results as compared to taper inserts in terms of adhesion and failure stresses. A linear elastic fracture model is also developed for the foam beam, and the fracture toughness is calculated. FEA analyses of the interaction between the inserts and the PU and sandwich composites under different loads were carried out. The FEA modeling results coincide with the experimental ones, hence validating the model.
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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".