Exact 3-D Stress and Stiffness Analysis of Functionally Graded Sandwich Plates Using Sampling Surfaces Method
Bibliographic record
Abstract
In the present work, the three-dimensional analysis for the deflection and stress distributions of functionally graded ceramic–metal sandwich plates is developed based on the method of sampling surfaces (SaS). In accordance with this method, into each layer of the plate, reference surfaces that are not equally spaced and are parallel to the mid-surface of the plate are introduced, and the displacement vectors of these surfaces are chosen as unknown functions. Such a choice allows the representation of the governing equations of the proposed higher order layer-wise plate theory in a very compact form and also permits the derivation of strain–displacement relationships correctly describing all motions including the rigid-body motions of the functionally graded plate. Hence the 3D elasticity problem of the thick plate is efficiently solved. The material properties of sandwich plate’s face layer are assumed to be that of a two-constituent material that vary continuously through the thickness of the face sheet according to a power law distribution of the volume fraction of the constituents. The core layer is homogeneous and made of an isotropic ceramic material. The effects of the volume fraction of the material constituents and their distribution on the deflections and, in particular, the 3-D stress distributions as well as the effects of the length-to-width and length-to-thickness ratios of the plate are investigated. Comparison of the results of the present work with the results available in existing literature is carried out for a benchmark problem. It is shown that considering large number of SaS, which are located at interfaces and Chebyshev polynomial nodes, the accuracy of the solutions can be improved significantly wherein the error will approach zero value as the total number of surfaces in each layer become very large.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".