Characterization of damage in non-crimp fabric glass fiber-reinforced reactive thermoplastic composites at low temperature using an in-situ digital imaging technique
Bibliographic record
Abstract
An in-situ digital imaging technique was developed to characterize damage in non-crimp fabric glass fiber/reactive thermoplastic cross-ply laminates subjected to tensile loading at −50 °C. A custom algorithm was developed to automatically detect the initiation and growth of 90° tow cracks, matrix cracks, and 0° tow cracks through image stacking, shift-correction, and thresholding. The laminates exhibited four stages of deformation/damage, including linear elastic, onset/growth of 90° fiber tow cracks, onset/growth of 0° fiber tow cracks, and progressive failure of 0° fiber tows. Although at low temperature the effective laminate strength and stiffness increased by 4 % and 13 %, respectively, damage initiated sooner and propagated at a higher rate leading to a 60 % increase in crack density at saturation. The digital imaging technique proved to effectively detect local damage in the glass fiber/thermoplastic laminates, which led to a deeper understanding of their low-temperature deformation response, damage characteristics, and damage tolerance.
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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".