A stress-relaxation approach to determine onset of delamination in angle ply laminates
Bibliographic record
Abstract
A new test method, named multi-relaxation test, is proposed for detecting on-set of delamination in fibre-reinforced polymers. Multi-relaxation test is based on the principle that uses change of stress relaxation behaviour of fibre-reinforced polymer to detect the occurrence of delamination. In this study, angle-ply laminated fibre-reinforced polymer (APL-FRP) is used to demonstrate and evaluate multi-relaxation test for detection of the delamination occurrence. The stress relaxation behaviour is characterized using a standard, three-element viscoelastic model in which the Eyring’s law is used to govern the time-dependent stress response to deformation. Results suggest a high possibility of using the trend line change of viscous stress at the beginning of stress relaxation to determine the critical stroke for the onset of delamination. The results also suggest that value for the corresponding static stress is very close to the value reported in the literature for APL-FRP of the same fibre lay-up. The major advantage of multi-relaxation test over other tests for the same purpose is that multi-relaxation test is able to detect delamination without relying on ancillary information such as acoustic signals. Therefore, multi-relaxation test can be used to characterize critical loading and deformation in fibre-reinforced polymer structures of any size and geometry, even when subjected to a loading mode that mimics the in-service loading.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".