Stress Analysis in a Multiscale Composite Laminated Plate with Cutout at the Centre Using Finite Element Method
Bibliographic record
Abstract
Orthotropic rectangular plates, featuring central cutouts and subjected to in-plane loading, are extensively employed across various engineering fields, including mechanical, automobile, aerospace, and marine.The introduction of a cutout inevitably leads to stress concentration within the plate.Incorporating carbon nanotubes (CNTs) into the polymer matrix composite has been noted to induce significant heterogeneity in stress fields.Functioning as bridges between the fibers and the matrix, CNTs can effectively mitigate stress concentration and enhance the damage tolerance of the composite.However, accurately determining stress concentration in CNT-based composites requires comprehensive understanding of the geometric discontinuity edge and well-defined evaluation techniques.Among these, the finite element method emerges as a straightforward yet precise approach for studying stress concentration around geometric discontinuities.The present work harnesses the finite element method to investigate stress concentration in CNT-based multiscale composite plates (Glass Fiber/CNT/Epoxy) with central cutouts under static in-plane loading.To validate the model, the derived results are compared with analytical data from conventional composite materials.Moreover, the impact of cutout size on stress concentration is examined for three distinct configurations: plates with a central circular cutout, plates with an elliptical central cutout (major axis in the longitudinal direction), and plates with an elliptical central cutout (major axis in the transverse direction).Preliminary findings suggest a correlation between increased cutout size and augmented percentage reduction in stress concentration.Notably, the maximum percentage reduction in stress concentration is observed in the case of plates hosting an elliptical cutout at the center with the major axis aligned in the transverse direction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| 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".