Computational simulation of impact perforation of polymeric-foam core sandwiched composites with different skin–face configurations
Bibliographic record
Abstract
The normal and oblique impact perforation responses of composite sandwich panels based on a Rohacell polymeric-foam core are numerically investigated at high impact energies (>60 J). A cylindrical form factor with a diameter of 140 mm and a thickness of 15 mm is selected for the sandwich specimens. Four different stacking sequences of 1 mm carbon/epoxy face sheets are considered (i.e., quasi-isotropic, cross-ply, angle-ply, and unidirectional stacking). A computational model was constructed using LS-DYNA finite element software and an inverse perforation testing scheme adapted with a split Hopkinson bar and confirmed by comparing these results with those obtained using the free shooting projectile-target testing schemes published in the literature. The effects of impact energy, failure modes, impact angles, and damage key parameters are analyzed. The results reveal the contact force versus displacement curves are highly influenced by the impact energy increases. The stacking sequence of the face sheets does not influence the energy absorption capacity. However, the maximum absorbed energy increases with an increasing impact angle up to 20°. Using Hopkinson bars in conjunction with the virtual inverse perforation testing approach is effective for examining the response of sandwich composites at high impact energies.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".