Micromechanical analysis of longitudinal and shear strength of composite laminae
Bibliographic record
Abstract
The analysis of several micromechanical models for estimating strength of composite laminae is presented. Longitudinal tensile, compressive and in-plane onset shear strengths are analytically estimated and compared with experimental data available in the literature. The tensile longitudinal load predominantly induces rupture of fibers. On the other hand, the compressive strength is highly influenced by fiber misalignment, inducing a wide range of failure mechanisms. The material response to in-plane shear presents a strong nonlinear response. The estimation of longitudinal tensile strength based on the rule of mixture approaches is compared with 27 experimental data. A novel improvement is proposed assuming that in situ strength of fiber is smaller than fiber strength measured individually due to manufacturing induced damage. For the in-plane shear, 6 models are compared with 10 experimental stress-strain curves, including a novel closed-form expression based on the concentric cylinders model. Finally, for the longitudinal compressive strength, 8 micromechanical models, including a novel model to estimate misalignment effect in fiber crushing, are compared with 61 experimental data are analyzed. Results indicate that the minimal average error for the longitudinal tensile strength is 12.4% while for the compressive strength it is 15%. For the shear strength, the closest prediction depends on the strength definition and the proposed damage onset strength presents the best predictions. In general, the newly proposed models present the best estimations compared with the other models.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".