A technique for in-situ detection of random failure in composite structures under cyclic loading
Bibliographic record
Abstract
A technique to detect the random failure in composite structures is presented. The epoxy matrix material is made conductive by the incorporation of carbon nanotubes. The modified matrix is used to fabricate glass fiber/epoxy/carbon nanotubes composite panels. Conductive grid points made from silver-epoxy paste are attached on the surface of the composite panels, so that electrical resistances in the regions between the grid points can be measured. The increase in electrical resistance between grid points is used to determine the increase in deformation (and possibly cracks) at the region between the grid points. It is found that the location of maximum increase in electrical resistance jumps from point to point as the number of cycles during fatigue loading is increased. This shows the random nature of the development of damage in the composites. The technique can detect the occurrence of early failure, usually in the matrix materials. The result of this work brings out the random nature of the early failure in composites. This also sheds light into whether the concept of crack initiation in composites is valid, since the early cracks jump around. When the paths of the final cracks reach stability, there is rapid crack propagation leading to fast final failure.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".