Investigating the role of aligner material and tooth position on orthodontic aligner biomechanics
Bibliographic record
Abstract
The primary objective of this work was to investigate the effect of material selection and tooth position on orthodontic aligner biomechanics. Additionally, material property changes with thermoforming were studied to elucidate its role in material performance in-vitro. An orthodontic simulator (OSIM) was used to evaluate forces and moments at 0.20 mm of lingual displacement for central incisor, canine and second premolar using Polyethylene terephthalate (PET), Polyurethane (PU) and Glycol-modified polyethylene terephthalate (PET-G) materials. The OSIM was scanned to generate a model used to fabricate aligners using manufacturer-specified thermoforming procedures. Repeated measures of MANOVA was used to analyze the effect of teeth and material on forces/moments. The role of thermoforming was evaluated by flexural modulus estimated by 3-point bend tests. Pre-thermoformed and post-thermoformed samples were prepared using as-received sheets and those thermoformed over a simplified arch using rectangular geometry, respectively. Groups were compared using Two-way ANOVA. The PET, PU, and PET-G materials exerted maximum buccal force and corresponding moments on the canine. PU exerted more buccal force than PET-G on the canine and second premolar, and more than PET on the second premolar. The impact of thermoforming varied according to the specific polymer: PET-G remained stable, there was a slight change for PET, and a significant increase was noted for PU from pre-thermoformed to post-thermoforming. The results of this study elucidate the influence of material and arch position on the exerted forces and moments. Further, the mechanical properties of thermoplastic materials should be evaluated after thermoforming to characterize their properties for clinical application.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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 teacher head, 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".