Comparison of the Low-Velocity Impact Responses and Compressive Residual Strengths of GLARE and a 3DFML
Bibliographic record
Abstract
The impact performance and compression after impact characteristics of 2D and 3D fiber metal laminates (FMLs) are investigated both experimentally and numerically. Commercial-grade GLARE3A-3/2-0.3, and a recently developed FML, which incorporates a unique 3D glass fabric, are used in the study. Both FMLs have similar areal densities. The specimens are subjected to impact loading at three energy levels-low, intermediate, and high. The test results indicate that GLARE is slightly more resilient under impact compared to the 3DFML. However, since GLARE is much thinner than the 3DFML, the two-material systems exhibit very different failure modes. GLARE and 3DFML lost up to 62.6% and 41.5% of their original compressive load-bearing capacity, respectively. Robust and accurate finite element models are developed that can predict the damage evolution and failure modes of both FMLs. Knowing the level of reduction in the residual load-bearing capacity of a material resulting from an impact is of practical importance when assessing the service life of materials. However, further exploration would be required to determine how the information obtained through testing relatively small-sized specimens in a laboratory environment can be extrapolated to larger real-life structural components.
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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 | 0.001 | 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) | 0.003 | 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".