A comparative finite element analysis of maxillary expansion with and without midpalatal suture viscoelasticity using a representative skeletal geometry
Bibliographic record
Abstract
The goal of this investigation was to adapt and incorporate a nonlinear viscoelastic material model representative of the midpalatal suture's viscoelastic nature into finite element analysis simulations of maxillary expansion treatment. Step-wise displacements were applied to a partial skull geometry to simulate treatment using an expansion screw appliance. Four simulation cases were considered for the midpalatal and intermaxillary sutures: 1. Neglecting suture tissue; 2. Linear elastic properties; 3. Viscoelastic properties; 4. A fused intermaxillary and viscoelastic midpalatal suture. Results from simulations indicated that removal of suture tissue and inclusion of viscoelastic properties resulted in the same maxillary displacement following 29 activations of 0.125 mm applied directly to the maxilla; however, assuming a fused intermaxillary suture significantly changed maxillary displacement patterns. Initial stress results within the suture complex were significantly influenced by the inclusion of suture viscoelasticity as compared to linear elastic properties. The presented study demonstrates successful incorporation of suture viscoelasticity into finite element analysis simulations of maxillary expansion treatment, and elucidates the appropriateness of various suture material property assumptions depending desired research outcomes.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".