Effect of thermal gradient on structural relaxation and characterization of thermal stresses in dental porcelains – A finite element study
Bibliographic record
Abstract
Zirconia-based materials veneered with glassy porcelain have become the material of choice for both dentists and their patients. This restoration material is now extensively used in clinical practice for the following reasons: material closely resembles tooth appearance, biofunctionality, biocompatibility and affordability. However, premature clinical failures due to the combination of subsurface flaws and tensile stresses within porcelains raise a concern about their reliability and integrity. This study evaluates structural relaxation in dental porcelain to form a suitable explanation for subsurface tensile stresses. Finite Element Method (FEM) is used for this analysis. User material subroutines, UEXPAN, and UTRS, were developed and integrated into a finite element solver to study the effects of structural relaxation on thermal stresses in veneer during final heat treatment. The predictions of the model were validated through qualitative and quantitave means. A validated model was used for Finite Element Analysis (FEA). Faster cooling rates and high veneer thickness predicted high subsurface tensile stresses due to ineffective structural relaxation. Slow cooling rates and lower veneer thickness showed desired compressive stresses in subsurface areas with visible structural relaxation. This work, emphasizing structural relaxation in veneering dental porcelain, provides a way forward for effective designing of dental restorations. Accordingly, it is useful to tailor the desired stress state for extended life in veneered dental porcelains on zirconia frameworks.
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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) | 0.001 | 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".