Characterization and numerical simulation of laminated glass fiber–polyester composites for a prosthetic running blade
Bibliographic record
Abstract
The objective of this work was to explore different types of deformations (buckling, bending, and relaxation) on the properties of laminated composites based on polyester as the matrix and glass fiber in two forms: woven and chopped strand mat. The specimens were produced with the same thickness but with different number of ply. Also, a thin gelcoat based on clay particles was applied on the chopped strand mat samples to get a third series. The results showed that using the same thickness, the mechanical properties, especially in terms of bending and buckling, are influenced by the layers’ number. Furthermore, a sports application, which is the main objective of this work, is presented as an applied investigation for a leg prosthesis. Three different running blades “Flex-foot Cheetah” were manufactured to be experimentally and numerically (ANSYS ACP software) characterized to simulate real conditions. The results showed a good agreement between the experimental and numerical values in terms of total displacement, which is around 50 mm, the produced blade has been tested in quasi-static and dynamic compression, and results showed that the relaxation behavior depends on the structure design and the used materials.
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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 | 0.001 | 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".