Effect of Different Heat Treatments and Surface Treatments on the Mechanical Properties of Nickel-Titanium Rotary Files
Bibliographic record
Abstract
This study aimed to compare the fatigue resistance of files made from different heat treatment methods and surface treatment. Four prototype files were created through heat treatment and titanium coating surface treatment (AT, DT, ER, EN; named arbitrarily by the manufacturer) at different times and temperatures. Artificial canals with curvatures of 45- and 90-degree were used for the fatigue testing. The files were operated at the speed of 500 rpm at 37 °C, and the time until fracture incurred by a 4-mm dynamic pecking motion at a speed of 8 mm/s was measured, and the number of cycles to failure (NCF) was calculated by applying rotation speed and time. The length of the fractured fragment was measured. The fractured specimens were observed under the SEM to compare the characteristics of fatigue fracture patterns. Differential scanning calorimetry analysis was performed to estimate the phase transformation temperature. One-way ANOVA with Duncan’s post-hoc comparison, the Kruskal–Wallis test, and Mann–Whitney U were applied to compare the fatigue resistance among the prototypes at a significance level of 95%. Regardless of the canal angle, the EN showed the highest fatigue resistance (p < 0.05). AT had the lowest NCF at the 90-degree canal (p < 0.05). ER had a higher NCF than the DT at 45 degrees (p < 0.05), but there was no difference at 90 degrees. DSC analysis revealed that the ER and EN groups exhibited two austenite peaks above 40 °C. In conclusion, the file that underwent a specific temperature heat treatment with titanium coating surface treatment showed the highest fatigue resistance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".