Finite Element Analysis of Screw-Tightening Torque Applied to Custom and Conventional Abutment
Bibliographic record
Abstract
The aim of this study was to design an abutment with an esthetic emergence profile contour using CAD technology and compare the stress distribution within the structure between the custom abutment and conventional abutment according to the screw tightening torque using 3D finite element analysis (FEA). The maximum tensile principal stress was found in the endpoint of the screw head and the start point of the screw line with regard to the application of the tightening torque of the screw. A similar pattern was observed in all of the following screws: 10N·cm, 20N·cm, and 30N·cm. The tightening torque of the screw had a significant impact on the changes in the stress of the abutment and screw fixture. This study also found that the condition in which the screw load was applied showed a more realistic description of the behavior of a single fixed dental implant than the condition in which the screw load was not applied. This study examined the optimal tightening torque value of the screw for the denture used in this study at a location slightly higher than 20N·cm. The difference in the custom abutment and conventional abutment did not have a significant impact on the supporting bone with regard to the external load. In regard to the stress occurring in the screw, the custom abutment had a lower degree of stress than the conventional type. Therefore, a screw fracture would occur less frequently in a custom abutment than a conventional abutment.
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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 | 0.001 |
| 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 | 0.000 | 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".