Design Optimization of Multilayer Ballistic Plates Using Steel Bearings for Impact Energy Dispersion and Projectile Trajectory Deflection
Bibliographic record
Abstract
This research aims to optimize the design of anti-ballistic plates by combining highstrength materials and geometric structures capable of changing the projectile contact angle.The materials used include perforated 304 stainless steel plates, steel ball bearings of varying diameters (3 mm, 4 mm, and 5 mm), resin adhesive, Hardox 450 plates, and rubber coatings of different thicknesses (4 mm, 5 mm, and 6 mm).The plate structure was designed in layers using a laminating method to increase the absorption of impact energy and reduce projectile penetration.This study used physical experimental methods, including weight fraction testing, ballistic tests with 5.56 × 45 mm caliber bullets, and morphological analysis using a stereo zoom microscope.The purpose of these tests was to evaluate the penetration depth, back face deformation (Back Face Signature), and projectile trajectory changes due to the design structure.The results show that variations in material configuration and ball bearing size significantly affect the armor's ability to block penetration.The plate configuration with 3 mm ball bearings and 6 mm rubber showed the lowest deformation and effectively changed the projectile path.This research contributes both theoretically and practically to the development of lightweight and effective ballistic protection, with potential applications in the military and civilian sectors.The findings provide a basis for further advances in armor technology based on structural and composite materials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".