Experimental Characterization of the Bearing Strength of Fiber Metal Laminates
Bibliographic record
Abstract
A novel experimental methodology extended from ASTM D953 was developed and implemented to conduct pin bearing experiments on GLARE3-5/4-0.3 and GLARE3-4/3-0.3 variants with the aim to examine a bearing load configuration from a local perspective as well as a global one. This was accomplished by introducing previously unconsidered testing fixtures and additional instrumentation, including bonded strain gages, as a means of measuring the local strain field generated by a bearing load. Load—displacement curves were produced as per the standard but were subject to numerous plateaus and significant statistical scatter, attributed to pin seating and global displacement measurement. Conventionally defined bearing yield strengths were calculated but lacked physical meaning, prompting the production of novel bearing strength vs. measured strain profiles. These new profiles, derived from the additionally acquired data, were void of the plateau anomalies and depicted a well defined trend, indicative of a more complete characterization of material response. Examination of these profiles resulted in a new, more intuitive definition of bearing yield strength that incorporated influence from the entire curve rather than idealizing it bilinearly. The phenomenological basis of the new definition suggests an analogous extension to additional methodologies such as standard tensile or compressive tests.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".