Mechanical and Numerical Analysis of Polymer-Natural Fiber Composites for Denture Applications
Bibliographic record
Abstract
Removable complete dentures are still a therapy of choice for a variety of medical professionals and patients even in an era of implant and fix prostheses.This article focuses on comparing complete dentures manufactured using various denture base materials.Heat-cured polymethylmethacrylate, used for prosthetic complete denture composites, was blended separately with Polyamide (PA) type 6 and Polyvinylpyrrolidone (PVP) type K30.These blends were prepared with various weight fractions (0%, 2%, 4%, and 6%) and reinforced with sisal and coconut powders, each added individually with varying weight fractions (2%, 4%, and 6%).The tensile test was carried out to achieve tensile strength, modulus of elasticity, and elongation percentage values.The numerical part depends on the Finite Element Method (FEM), conducted by using Ansys Workbench-2020 R2.According to the experimental data, the tensile strength, elastic modulus, and elongation of polymer blends increase at a 2% weight fraction of PA and PVP particles, and then decrease with higher PA and PVP particles' weight fraction.However, they decrease with increasing weight fraction of coconut and sisal particles.The highest tensile strength and elastic modulus are 86 MPa and 2.531 GPa, respectively, for PMMA-2% PA, and the greatest elongation percentage is 5.28% for PMMA-2% PVP.These findings lead to the conclusion that the addition of polymer blend materials to PMMA resin is a promising approach for improving tensile properties in applications such as complete or partial denture bases, addressing an ongoing challenge.
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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 | 0.001 | 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) | 0.002 | 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".