On the Pin Bearing Behavior of Orthotropic Fiber Metal Laminates
Bibliographic record
Abstract
Experiments enforcing a pin bearing loading configuration were performed on a fully orthotropic GLARE 4 variant. The protocol employed in such experiments stemmed from a similar methodology performed on quasi-isotropic GLARE variants though now incorporating a local measurement scheme using biaxial strain gauges rather than uniaxial ones. The aim of this local measurement was both the extraction of novel bearing yield strength values and the detection of buckling within the aluminum layers. The encouragement of delamination and buckling was key since they not only form an integral portion of the proposed yielding through delamination buckling (YDB) mechanism but in addition, the pin bearing configuration — which ipso facto, encourages the former(s) — has been identified in the literature as the most conservative and accurate means for analyzing joint behavior and collapse. Analytical calculations previously performed support the empirical findings and provide direct evidence for the hegemony of aluminum yield strength in joint collapse. The proposed and employed protocol has been shown to be effective across a comprehensive range of GLARE variants and may be extended analogously to other standardized testing methodologies.
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 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) | 0.002 | 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".