Simulation of dental collisions and occlusal dynamics in the virtual environment
Bibliographic record
Abstract
Semi-adjustable articulators have often been used to simulate occlusal dynamics, but advances in intra-oral scanning and computer software now enable dynamics to be modelled mathematically. Computer simulation of occlusal dynamics requires accurate virtual casts, records to register them and methods to handle mesh collisions during movement. Here, physical casts in a semi-adjustable articulator were scanned with a conventional clinical intra-oral scanner. A coordinate measuring machine was used to index their positions in intercuspation, protrusion, right and left laterotrusion, and to model features of the articulator. Penetrations between the indexed meshes were identified and resolved using restitution forces, and the final registrations were verified by distance measurements between dental landmarks at multiple sites. These sites were confirmed as closely approximating via measurements made from homologous transilluminated vinylpolysiloxane interocclusal impressions in the mounted casts. Movements between the indexed positions were simulated with two models in a custom biomechanical software platform. In model DENTAL, 6 degree-of-freedom movements were made to minimise deviation from a straight line path and also shaped by dynamic mesh collisions detected and resolved mathematically. In model ARTIC, the paths were further constrained by surfaces matching the control settings of the articulator. Despite these differences, the lower mid-incisor point paths were very similar in both models. The study suggests that mathematical simulation utilising interocclusal 'bite' registrations can closely replicate the primary movements of casts mounted in a semi-adjustable articulator. Additional indexing positions and appropriate software could, in some situations, replace the need for mechanical semi-adjustable articulation and/or its virtual representation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".