Using 3D printing to fabricate realistic test projectiles for natural fragmentation from buried charges
Bibliographic record
Abstract
Buried charges such as improvised explosive devices continue to be one of the most lethal and hidden threats service members face. On detonation, ground debris near the blast area is accelerated towards service members as secondary fragmentation, consisting of sand, gravel and rocks. In order to mitigate injury, protective equipment can be worn, yet it is difficult to gather accurate data for engineering decisions when the standard test uses a fragment simulating projectile made from metal. It is difficult to test secondary fragmentation from ground debris due to the natural heterogeneity and variance of the material. A methodical and reproducible method of testing fragmentation damage from ground debris was developed to study and improve protective equipment against natural secondary fragmentation. We present herein the novel process of 3D-printing ballistic projectiles from silica sand, followed by launching with an air canon. Outlined within are the successes, challenges and proposed implementations of the technology. The 3D-printed sand projectiles achieved speeds over 170 m/s, resulting in measurable damage to single Kevlar sheets. Other flight parameters such as yaw and rotation were captured, resulting in observations about design and shape of the projectiles. It was found that one design performed better in terms of velocity, rotation and impact. The technology has the potential to disrupt the protective equipment sector by providing a controlled means of assessing natural fragmentation damage.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".