Determination of the Monoclinic Properties of Human Tooth Enamel Microstructure by a Periodic Three Dimensional Finite Element Model
Bibliographic record
Abstract
Researchers have reported diverse mechanical properties (Young's modulus) of human tooth enamel from experiments and finite element simulations, because of the complicated microstructure, which contains variations in crystal orientations and non-homogeneous properties. Although past models have effectively considered the microstructural effects, appropriate conditions for introducing crystal orientations within enamel rods and the property variations between rods and the interrod enamel are still necessary.In this thesis, the micromechanical response of the enamel microstructure is investigated using a periodic finite element model to determine the effective monoclinic mechanical properties and determine localized effects of microstructure on the stress field. A spherical micro-indentation test was conducted on the bulk enamel model and the effective homogeneous model. The difference in response to indentation loading between the heterogeneous and homogeneous models revealed changes related to the enamel microstructure.The model can be used to consider changes in effective properties of enamel based on microstructural variations, which can be applied to restorative materials attached or embedded within enamel. The study of the influence of microstructure on the damage generation and failure modes of enamel can also be accomplished using the model, which may be due to fractures and the abrasion-erosion wear process.
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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 | 0.000 |
| 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) | 0.001 | 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".