Finite element simulation of disk‐shaped HfB <sub>2</sub> ceramics during spark plasma sintering process
Bibliographic record
Abstract
Abstract Spark plasma sintering is an interesting manufacturing method, which benefits electric current heating. In the present study, the sintering process of hafnium diboride (HfB 2) ceramics was simulated using the finite element method. The governing equations of electric potential, current conservation, and energy conservation were solved using COMSOL Multiphysics software. The electric potential of the whole system versus time and current distribution at different time steps were obtained. The heat generation was calculated using the Joule heating effect, and the needed temperature for sintering was obtained. The required temperature for process progression was 2100°C that achieved after 1380 s. Moreover, the maximum electric current was 5942.863 A, which remained constant until the end of the operation. Since HfB 2 is an electrical conductor, the current easily passed through the sample with an approximately uniform distribution. To provide a better understanding of the sintering mechanisms, an electrically resistive material (Al 2 O 3 ) was also simulated. The results showed a more uniform current distribution in HfB 2 compared to Al 2 O 3 . It was observed that the needed temperature for sintering of alumina was obtained only by heat conduction from graphite die toward the sample, whereas HfB 2 benefited both mechanisms of heat conduction and Joule heating inside the sample.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".