Low‐velocity impacts in continuous glass fiber/polypropylene composites
Bibliographic record
Abstract
Abstract The low‐velocity impact behavior of a continuous glass fiber/polypropylene composite was investigated. Optical microscopy and ultrasonic scanning were used to determine the impact‐induced damage. At low impact energy, the predominant damage mechanism observed was matrix cracking, while at high energy the damage mechanisms observed were delamination, plastic deformation, which produced a residual specimen curvature, and a small amount of fiber breakage at the edge of the indentation on the impacted face of the specimens. The impact load vs. time signals were recorded during impact and showed that the load corresponding to the onset of delamination was independent of the impact energy in the range tested. The load at which the onset of delamination occurred corresponded to the values obtained by performing a linear regression of the delaminated area, obtained by ultrasonic scanning, as a function of the impact force. Tensile and flexural tests performed on impacted specimens showed that the tensile and flexural residual strengths and the flexural modulus decreased with increasing incident impact energy, while the post‐impact residual tensile modulus remained constant. The dynamic interlaminar fracture toughness was evaluated from the critical dynamic (impact) strain energy release rate of specimens with a delamination simulated by an embedded insert. The results are compared with the interlaminar fracture toughness values obtained during subcritical steady crack growth.
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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) | 0.002 | 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".