The Effects of Micro- and Nano-Fillers’ Additions on the Dynamic Impact Response of Hybrid Composite Armors Made of HDPE Reinforced with Kevlar Short Fibers
Bibliographic record
Abstract
Hybrid composite armors consisting of Kevlar short fibers reinforced high-density polyethylene were prepared and the effects of the addition of micro and nano-fillers on the dynamic impact response and the energy absorption under ballistic impact were investigated. Five groups of specimens were manufactured using compression molding of pellets containing mixtures of high-density polyethylene and the reinforcing materials. The first group consist of high-density polyethylene reinforced with 10 wt% Kevlar pulp (KN-1). The rest are hybrid composites created by the addition of 20 wt% of micro and nano-fillers. The natural micro-fillers used are particles of chonta palm wood (KN-2) and potato flour (KN-3). The synthetic nanofillers are colloidal silica (KN-4) and gamma alumina (KN-5). Microstructure (scanning electronic microscope) and compositional (energy-dispersive spectroscopy) analysis of the hybrid composites were carried out to evaluate matrix-reinforcements-interface. The fabricated composites plates were subjected to high velocity impact using split Hopkinson pressure bar system and ballistic impact, according to NIJ standard–0101.06 for ballistic resistance. Significant stiffness improvements of up to 43.5% were achieved as a result of the addition of synthetic nano-particles to Kevlar fiber reinforced high-density polyethylene. X-ray diffractometer analysis revealed that the crystalline structure of the Kevlar reinforced high-density polyethylene is unaffected by addition of the nano-particles as fillers. However the intensity of the crystalline peaks decreased depending on the type of the added fillers. The results of dynamic impact test using split Hopkinson pressure bar revealed improved impact resistance by addition of synthetic nanofillers (silica and alumina). The results of the ballistic impact test showed the gamma alumina nano-particles (KN-5) exhibited the highest energy absorption capability. The results of these investigations indicate that hybridization Kevlar short fibers reinforced high-density polyethylene by micro and nano-fillers addition enhances the stiffness, impact resistance and ballistic energy absorption capability of the composites.
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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".