The influence of functionally graded graphene reinforcements on thenonlinear vibration of postbuckled multiple through-the-width delaminationsof layered composite plates
Bibliographic record
Abstract
The nonlinear thermal stability behavior and large amplitude vibration of functionally graded graphene-reinforced composite (FG-GRC) laminated plates with single and multiple through-the-width delaminations subjected to a uniform temperature rise are discussed in this article. The thermo-mechanical properties of the GRCs are estimated using the extended Halpin-Tsai micromechanical model that incorporates efficiency parameters to take into account the nanoscale size and surface effects of the graphene. The von Karman geometrical nonlinearity is adopted in a solution based on the layerwise third-order shear deformation theory (TSDT). The nonlinear equilibrium equations derived by the minimum total potential energy principle solved using the Ritz method in conjunction with the Newton-Raphson iterative procedure. A three-dimensional finite element model was also developed using ABAQUS to compare the accuracy of the results obtained using the layerwise TSDT. Parametric studies reveal that the types of the graphene distribution pattern and configurations of the delamination zones have a substantial effect on the thermal equilibrium paths and variation of the fundamental frequencies in the pre/post-buckled domains. Moreover, the thermal stability responses of the simply-supported delaminated composite plate with symmetric graded graphene patterns are entirely different from those with asymmetric graphene dispersion. Furthermore, because to the existence of interlaminar flaws (delamination), the variation of fundamental frequencies in both pre-and post-buckled domains differs significantly from those in perfect plate (without any delaminations)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".