Performance Optimization of Composite Pylons in Transtibial Prostheses Using Nanoparticles SIO2: A Comparative Experimental and Numerical Study
Bibliographic record
Abstract
This paper addresses the research gap.Many studies have addressed the addition of nanomaterials, but none have examined their impact on the manufacture of prosthetic limbs, particularly those made of prosthetic pylon.It aims to evaluate the mechanical and physical effects of adding silicon dioxide (SIO2) nanoparticles to a hybrid composite material consisting of glass fibers, Perlon and a polyester matrix.The novelty of this work using nanoparticles SIO2 for enhancing composite materials for manufacturing prosthtic.Methodologically, two experimental sets were fabricated: a reference specimen (∆1) and a nano-specimen (∆2), using a vacuum-assisted resin infusion technique.The mechanical properties were characterized through tensile and flexural tests, and the structural performance was then evaluated through an experimental buckling test of the critical buckling load of the pylon for material (∆2), which was improved by approximately 65% after the addition of SIO2 compared to the material (∆1).The results were compared with a theoretical model and a finite element model (FEM) built in ANSYS to simulate the critical mid-stance phase of the gait cycle.The results demonstrated a clear superiority of the nanomaterial, with the ultimate tensile strength increasing by more than 41% and the elastic modulus by nearly 54.4% compared to the reference sample.Furthermore, the manufactured pylon was 44% lighter and 40% less expensive to manufacture compared to its Al-6061 counterpart.
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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".