Investigating the Influence of Nanosilica and Fiber Layer Sequence on Interlaminar Shear Strength in Carbon-Kevlar-Epoxy Polymer Hybrid Nanocomposite
Bibliographic record
Abstract
Present research work concentrated investigating the influence of adding nanosilica with epoxy matrix in varying weight percentages such as 0, 0.5, 1.0 and 1.5 on interlaminar shear strength in fiber reinforced polymer hybrid nanocomposite and fiber layer sequence.The polymer hybrid nanocomposite is having five carbon, four Kevlar layers (5C4K) and five Kevlar, four carbon (5K4C) layers of woven fibers.High speed shearing technique was used for the better dispersion of nanosilica with epoxy resin.Vacuum assisted resin infusion molding technique was used to fabricate the hybrid polymer nanocomposite laminates.Post curing was carried out effectively.Interlaminar shear strength test carried out according to ASTM D2344.The tested specimens show that 0.5 weight percentage of nanosilica with epoxy provides higher interlaminar shear strength than other weight percentage of nanosilica in both types of stacking sequences.The fiber layer sequence 5C4K shows better interlaminar shear strength as compared to 5K4C.
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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".