State Stress Analysis of Dental Restoration Materials Using the ANSYS Program
Bibliographic record
Abstract
Dental restorations that successfully bind to dental tissue and cosmetically mimic the tooth are in higher demand.Dental professionals can reconstruct posterior teeth using inlays/onlays, which combine functional and anatomical factors with aesthetic considerations.Inlays/onlays are made of porcelain and resin with no metallic basis.In this research, cases of deformation and the distribution of different stresses and strains for a tooth second upper molar crown were studied by designing four two dimensional mathematical models, the first for a tooth made of natural materials, and for the other three mathematical models of teeth with fillings from different materials (Zirconia, Titanium, Ceramic), and impact force was applied in three places, the first in the middle the filling and the second in the contact area between the filling and the tooth, and the third force shed between the first force and the second force.The results showed that the least deformation was in the model containing the zircon filler, while the highest deformation was in the model containing the ceramic filler.As well as from the important results, it was found that the model containing the ceramic filler is in a suitable fit with the model of the natural tooth in terms of the distribution of stresses, strains and deformation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".