Prediction of Bearing Strength in Fiber Metal Laminates
Bibliographic record
Abstract
Finite element (FE) analyses are carried out on bolt bearing testing scenarios based on data found in the literature. Both layer-by-layer and smeared property FE models are created to calculate the compressive characteristic dimension (CCD) for three GLARE variants. A novel re-definition of conventional CCD is proposed which is governed by the yield strength of aluminum. The new definition also incorporates the two-phase nature of GLARE, as well as the delamination/ buckling phenomenon for pin/bolt bearing, in a bearing failure mode. A previously unconsidered, orthotropic plate buckling analysis is also conducted in a conservative, worst case scenario sense on the laterally unsupported prepreg layers. Results of the buckling analysis suggest that the prepreg contribution to bearing strength, in a bearing failure mode, is at best negligible and joint collapse is governed by the yielding and delamination of the aluminum layers. Calculation of a CCD, based on the new yield strength definition, produced consistent values amongst all GLARE variants considered in the layer-by-layer analysis suggesting that the CCD is a property of the material alone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.000 | 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".