A Numerical Approach to Estimate First Ply Failure of Fibre Metal Laminate
Bibliographic record
Abstract
Fibre Metal Laminates (FMLs) are laminates consisting of metal layers and fibre reinforced composite layers. These laminates are designed to improve some specific properties of constituent metals and composites layers. Estimation of First Ply Failure (FPF) Loads of these FMLs is a part in the broad characterization of these materials. A numerical method is developed for the estimation of FPF when these laminates are used as simply supported plates subjected to uniformly distributed load. Various failure criterions are used to identify these loads. The proposed method has been validated with the results of exact (Navier) solution available in the literature. FPFs are estimated for different groups of FMLs based on Aluminum, Titanium and Magnesium layers. The results are presented in the form of non-dimensional FPF and deformation values for various aspect ratios.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".