Experimental investigation on the effects of stainless-steel mesh reinforcing layers on low-velocity impact response of hybrid thermoplastic glass fiber composites
Bibliographic record
Abstract
This study aims to assess the hybridization effect on the perforation threshold of Low-Velocity Impact (LVI) in thermoplastic glass composite laminates, incorporating layers of resin-impregnated stainless-steel mesh. Reinforcing methodologies such as hybridization are recently being adopted as a practical approach to increasing the energy-absorbing capacity of polymer composites. In the current paper, a multi-step hot press lamination method has been employed to fabricate the hybrid composite laminates strengthened with stainless-steel mesh layers. Several stacking sequences, metal mesh wire sizes, orientation and position relative to the impactor have been examined under various LVI energies. It was revealed that the LVI penetration energy was increased for the thermoplastic-based composite laminates reinforced with stainless-steel mesh layers. Furthermore, the LVI penetration energy threshold was significantly influenced by the metal mesh wire size, orientation and stacking sequence. Finally, the backlight method capability was assessed to detect the after-impact interlaminar damages.
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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".