Layer Arrangement Impact on the Electromechanical Performance of a Five‐Layer Multifunctional Smart Sandwich Plate
Bibliographic record
Abstract
This chapter deals with the impact of layer arrangement on the design of five-layer multifunctional smart sandwich plates (5LMSSPs) under electromechanical loads. The layers of the considered smart plate are made of porous polymeric, functionally graded (FG) graphene platelet (GPL)/polymer nanocomposite and piezoceramic materials. The porous polymeric layer, which is the thickest layer, is considered as core to significantly reduce structural weight. Due to the brittle structure of piezoceramics, considering those layers as the outer layer is not always the best set of layer arrangement. By developing a mesh-free solution, a comparison study has been conducted to explore the impact of changing the location of piezoceramics from faces to middle layers on the electromechanical performances of 5LMSSPs. The mechanical properties of GPL/polymer nanocomposite layers are estimated by employing Halpin–Tsai equations to capture the shape of nanofillers. In the mesh-free method, the shape functions of moving least squares (MLSs) are used to approximate the displacement field of such smart plates. Displacement field is defined by an efficient third-order shear deformation theory (TSDT) of plates proposed by Reddy which has only five degrees of freedom. By minimizing total energy function, the mesh-free forms of the coupled electromechanical governing equations of 5LMSSPs are extracted. The impacts of layer arrangement and other design parameters on the electromechanical performances of 5LMSSPs has been investigated in this chapter.
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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) | 0.001 | 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".