Using a Negative Poisson's Ratio to Mitigate Stress Concentrations in Perforated Composite Plates
Bibliographic record
Abstract
Stress concentrations pose a significant challenge in designing and optimizing composite components, often resulting in structural alterations and crack formation. This study delves into a detailed numerical and analytical analysis of stress concentration factors in composite materials, with a specific focus on the influence of negative Poisson's ratio and out‐of‐plane modulus. By exploring the interaction of these material properties, the objective is to devise effective strategies for alleviating stress concentrations in composite structures. Through analytical and numerical simulations, a robust correlation between the customized Poisson's ratio and modulus and the mitigation of stress concentration factors, particularly in epoxy/graphite‐reinforced composites are established. This tailored approach has the potential to enhance energy absorption capabilities and consequently reduce the risk of crack propagation in perforated laminated composite plates. This research findings offer valuable insights into composite material design, presenting innovative solutions for enhancing structural integrity and reducing susceptibility to stress‐related issues.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".