Ballistic impact performance of hybrid composite armors made of aluminum foam containing the dispersion of shear thickening fluid made of various synthetic nano-fillers
Bibliographic record
Abstract
Hybrid composites armors made of closed-cell aluminum foam are developed for intended use in ballistic protective plates. The manufacturing process involved impregnation of shear thickening containing different micro and nano-fillers into aluminum foam panels which are subsequently bonded two AA 5086-H32 aluminum sheets that surrounded the targets, by using compression molding techniques. The effects of the addition of different nano-fillers as colloidal silica, gamma alumina, silica carbide, and Kevlar micro-fibers to the aluminum foam plates on the ballistic response of the hybrid composites armors were investigated. Scanning electron micrographs were used to investigate the interfacial interaction between specimen layers, and the influence of the nanoparticles impregnated within closed aluminum foam cells before and after high-velocity impacts. The ballistic impact resistance of the produced hybrid composite cell aluminum foam laminates was tested according to NATO standards using a semi-automatic 9mm Beretta Cx4 Storm Rifle Luger. The results indicated that the performance of the hybrid composite armors made of aluminum foam were enhanced by the deposition of micro and nano-fillers into the surface of the closed-cell aluminum foam.Therefore, the ballistic impact resistance and energy absorption of the specimens were improved. The highest impact energy absorption capacity was achieved by the deposition of Kevlar micro-fibers, but the resulting plates have the highest target weight and thickness. Silica carbide powder followed by gamma alumina, and colloidal silica powder in that order, enhanced the impact energy absorption capability with the least target weight and thickness average. These findings indicate that introduction of micro and nano-fillers coating on closed-cell aluminum foam, improved a range from 4.9 to 30.9 J in comparison with untreated samples, therefore, it could be a promising method for strengthening interfacial bonding between layers of the aluminum foam 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".