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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".