Dynamic characterization of viscoelastic materials used in composite structures
Bibliographic record
Abstract
The main focus of this paper is the experimental comparative analysis of the viscoelastic properties of acrylic- and silicon-based viscoelastic materials. These materials are widely used in the aeronautic industry for structural and/or damping applications. It is therefore required to determine their viscoelastic properties such as shear modulus and loss factor following their integration to aircraft composite structures. The influence of material thickness, bonding quality, curing and cocuring with composite material was evaluated using dynamic mechanical analysis machine in plan shear configuration. This comparative analysis in the 0–600 Hz frequency range provides useful information that should be taken into consideration when designing bonded joints for structural and/or damping applications. The small variation of the acrylic-based material loss factor over a wide range of frequencies suggests that this material is a good candidate for damping applications at room temperature in metallic structures where no curing is required. However, cocuring of acrylic-based material with composite laminae increases its shear modulus up to six times with respect to the uncured material whereas its loss factor is reduced by about 20%. On the other hand, the silicon-based material remains stable after thermal treatment (curing and cocuring) suggesting that it is well suited for in situ damping treatment involving laminate composite structure. However, at room temperature its shear modulus after cocuring is lowered by almost 25% due to poor bonding quality, which depends on the nature of the substrate.
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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 | 0.001 | 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".