Impactor shape effect on the shock mitigation behavior of metamaterial structures
Bibliographic record
Abstract
Auxetic metamaterial structures, characterized by their negative Poisson’s ratio, have shown promising mechanical performance, particularly in shock impact scenarios. However, the complex and scenario-dependent behavior of these structures necessitates comprehensive and appropriate evaluation and verification. In this study, the shock mitigation property of an auxetic planar design in relation to the shape of the impactor surface was discussed using numerical simulation results, validated using drop testing shock experiments. Results revealed that both metamaterial shape and impactor geometry significantly influenced behavior of metamaterial response. For the plate impactor, minimal deformation at lower velocities resulted in higher peak accelerations for metamaterial configurations. Conversely for the cylindrical impactor, metamaterial structures consistently enhanced shock mitigation across all impact velocities. An Artificial Neural Network (ANN) model was developed to predict the vertical direction acceleration of the impactor by also incorporating the shape of the impactor as an evaluation parameter, in addition to the metamaterial dimensions and impact velocity. Validation with impact scenarios outside the subset of the training dataset confirmed the ANN model’s accuracy, achieving at least 94% accuracy for both impactor cases, thereby offering an efficient alternative to traditional experimental and numerical simulations for studying metamaterial shock mitigation behavior.
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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".