Effect of connector width on stress distribution in all ceramic fixed partial dentures (A 3D finite element study)
Bibliographic record
Abstract
STATEMENT OF PROBLEM: Connectors in fixed partial dentures (FPDs) are the weakest areas and responsible for failure in most cases. Optimizing the design of connectors will lead to higher strength and better performance of all-ceramic FPDs. PURPOSE OF STUDY: The aim of this study was to use the finite element method in order to simulate the effect of connector width on stress distribution in all-ceramic FPDs. MATERIAL AND METHODS: Three 3-dimensional finite element models for a 3-unit FPD made of IPS-Empress 2 representing a lower first molar were created and a static load of 500 N was applied axially at mid pontic area. By choosing three different widths, 3 mm, 4 mm, 5 mm for connectors, three models I,II, and III for complete assembly of teeth and connectors were created. RESULTS: Maximum stress occurred in the connector area in all models. Compared to model I, stress decreased 24% in model III; so the wider connector lead to lower stress values. CONCLUSION: Connectors are the most regular area for the fracture in all-ceramic FPDs because of high concentration of stress. Decreasing the width of connector raises the stress and increases the risk for fracture. Also, maximum stress in bridges is less than half of the strength of IPS-Empress2 and no failure is expected for all cases. CLINICAL IMPLICATIONS: This in vitro study of 3-unit all ceramicFPDs made with IPS-Empress2 shows that an increase in the width of connector reduces the stress concentration and improves the likelihood of long-term prognosis. Also, IPS-Empress2 can be used in posterior regions in many cases.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".