Through-the-Thickness Compressive Strengths of Graphite/Epoxy Laminated Composites: Experimental Characterization and Statistical Analysis
Bibliographic record
Abstract
The purpose of the present paper is to investigate the through-the-thickness compressive strengths of graphite/epoxy laminated composites.The investigation is focused on three aspects of concern; probabilistic characteristics, effects of specimen lay-ups and effects of specimen sizes involved with the compressive strengths.One type of cross-ply prism specimen and four types of unidirectional prism specimens with different sizes are tested by directly applying the through-the-thickness compressive load on the surfaces of specimens through two pieces of collars.The inherent variability in the through-the-thickness compressive strengths is statistically studied based on experimental results.A contact finite element analysis is performed to look into the effects of friction between the collar and specimen and the effects of modulus of collar materials on the stress distribution inside the specimen.It is found that the through-the-thickness compressive strength strongly depends on the specimen lay-up, and depends on the specimen size to some degree in the unidirectional specimens.Moreover, the through-the-thickness modulus also seems to depend on the specimen lay-ups.The probability function of the through-the-thickness compressive strength is given in order to demonstrate the involved random behavior.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".