Bibliographic record
Abstract
Abstract The aerospace industry uses carbon‐fiber epoxy laminates for structures to reduce weight and increase payload. The “standard” design criterion for strength is that proposed by Tsai‐Wu. For stiffness, which is generally more critical than strength, classical laminated plate theory (LPT) is used. The normal lay‐ups considered for commercial aircraft are made up from 0°, 90° and ± 45° orientations. Angle ply laminates, [± ϕ] ns , with ϕ fixed to some angle such as 20°, are not normally used (although this type of structure is employed with great success in the pressure vessel industry). According to the Tsai‐Wu criterion, such a structure should be extremely weak, which probably accounts for the absence of simple angle ply structures in aerospace designs. However if short and wide samples (aspect. ratio 0.5 or less) are tested, higher values are obtained for modulus and much higher values for strength than the long narrow samples used to develop the Tsai‐Wu criterion. The short and wide sample test results are in agreement with results from tests on tubes. These observations show that there is an “edge softening” effect: long narrow samples have a relatively large amount of this soft edge. Since design software normally uses Tsai‐Wu and LPT, large errors in strength and significant errors in stiffness are possible at this stage, and better lay‐up designs may be totally missed. The experimental work leading to these conclusions is described and innovative designs are discussed.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".