Investigation on Effects of Geometric Design Variable and Biomaterial Analysis on Stress Distribution for One-Piece Dental Implant — A 3D Finite Element Analysis
Bibliographic record
Abstract
The objective of this research is to analyze the dissemination of stress in the bone surrounding orthopedic implants composed of various compositions of CFR-PEEK, a material that can be tailored with diverse physical, mechanical, and surface characteristics.Also, this study aims to compare the stress distribution between models constructed with PEEK components, GFR-PEEK, implants reinforced with 30% carbon fiber (30% CFR-PEEK), and implants reinforced with 60% carbon fiber (60% CFR-PEEK), considering different geometric variations.The one-piece dental implant was modelled using solidworks (CAD) software.A 3D FEA model was created to simulate the one-piece dental implant system and the surrounding bone.The model incorporated various geometric design variables, including implant length, diameter and thread pitch.Different loading conditions were enforced to assess the stress dissemination within the implant and bone.The 3D FEA simulations revealed that varying the geometric design variables of the one-piece dental implant significantly influenced the stress distribution.Moreover, the choice of biomaterial for the implant played a crucial role in stress distribution.The findings indicate that a 60% CFR-PEEK implant with continuous carbon fiber disperses pressures in a similar manner to a titanium implant.However, for optimal performance, the study suggests that a percentage of endless carbon fibers within the PEEK matrix below 60% would provide the most ideal elasticity while maintaining minimum deformability and minimal stress distribution during loading.It is important to consider the biological characteristics of the materials along with the study's results.For dental implants with specific parameters (0.8 mm single thread pitch and Type II bone quality), the study suggests that the next best option after a 60% CFR-PEEK material would be a 30% CFR-PEEK material.This is because the higher concentration of carbon fiber in the 60% CFR-PEEK material increases the risk of potential contact with individuals, posing a safety concern.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".