Local buckling mitigation and stress analysis of a shape memory alloy hybrid composite plate with and without a cutout
Bibliographic record
Abstract
In this study, the numerical model of a shape memory alloy hybrid composite (SMAHC) plate was constructed. The model was based on the effective coefficient of thermal expansion (ECTE) model of the shape memory alloy (SMA) material. The embedded SMA wires were modeled as an integrated composite layer in conjunction with the host composite lamina. The tailored design feature of laminated composite plates allows the flexibility of orienting the SMAHC layer along different directions. The response of the SMAHC plates with and without a central cutout, having certain geometric imperfections, subjected to an initial in-plane compressive loading and a subsequent elevated thermal load, were investigated numerically and experimentally. It was established that the positive attribute of the SMA material could be used to successfully suppress the post-buckling deflection of the SMAHC plate under an elevated temperature, when the SMA wires were properly oriented. The variation of stress concentration around the cutout of the SMAHC plate was also investigated. It was found from this study that the orientation of SMA wires had a significant influence on the suppression of the lateral deflection. The extent of such a positive attribute was found to be influenced by the boundary condition of the SMAHC plate.
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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".