Microgap Formation in Conical Implant‐Abutment Connections Under Oblique Loading: Influence of Cone Angle Mismatch Through Finite Element Analysis
Bibliographic record
Abstract
OBJECTIVES: This study evaluated different designs of the conical implant-abutment connection (IAC) and their resistance to microgap formation under oblique loads as specified by the ISO standard for testing dental implants. Also evaluated was the effect of deviations from the ISO specifications on the outcomes. METHODS: Finite element analysis was conducted to compare the microgap formation and stress distribution among three conical IAC designs (A, B, and C) in two loading configurations: one compliant with ISO 14801 and one with a modified load adaptor (non-ISO). The different IAC designs varied in the taper, diameter, and cone height. The cone angle mismatch (Cam) between the implant and abutment was considered. A torque of 20 Ncm and oblique loads (up to 400 N) were simulated. RESULTS: The stresses produced by the screw-tightening torque varied among the different IAC designs. The contact height was approximately 0.3 mm for Designs A and B, and less than 0.03 mm for Design C. Under oblique loads, Design A maintained IAC sealing without gap formation up to 400 N. With the ISO adaptor, gaps appeared in Design B at 300 N and in Design C at 90 N. The non-ISO adaptor resulted in gap formation at 160 N for Design B and at 50 N for Design C. CONCLUSIONS: The IAC design and cone angle mismatch significantly influenced microgap formation, with some designs showing zero gaps even when the oblique load reached 400 N. The non-ISO adaptor increased gap formation in IACs B and C.
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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 | 0.002 |
| 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 | 0.001 |
| 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".