Evaluation of numerical methods to model adhesives used in ballistic protection structures
Bibliographic record
Abstract
A numerical investigation is presented in this work to study the ballistic performance of Aluminum Oxide (Al203) ceramic plates bonded to a polycarbonate backing subjected to impacts from 7.62 mm diameter calibre projectiles. The ceramic plate measured 102 × 102 mm with a thickness of 5.8 mm. Numerical models were developed using the explicit finite element code LS-DYNA, which possesses contact, erosion and material failure capabilities particularly suitable for ballistic simulation. The Johnson–Holmquist, Piecewise Linear Plasticity and Plastic Kinematic constitutive material models were used to model the materials behaviors of the ceramic, projectile materials and backing material, respectively. The residual velocity and depth of penetration (DoP) into the backing material was obtained and compared with available experimental data. The simulation was capable of capturing the DoP into the backing with a high degree of accuracy compared to the experimental tests. The results showed that when the ceramic plates were impacted by a projectile, the damage mechanism of the fragmented ceramic included radial and circumferential crack lines along with a pulverized center and a highly eroded bullet. The failure of the bonding adhesive between the ceramic plate and polycarbonate backing was also accurately captured by using the plasticity compression tension material model to simulate the adhesive.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".