Effects of die geometry and insulation on the energy and electrical parameters analyses of spark plasma sintered TiC ceramics
Bibliographic record
Abstract
This work conducts a numerical simulation to investigate the temperature and electric current distribution during the spark plasma sintering (SPS) process using the finite element method (FEM) carried out in COMSOL Multiphysics software. The main goal is to optimize the SPS process for titanium carbide (TiC) ceramics, with a particular focus on the effects of insulation and die geometry (height and thickness). For the TiC material, the ideal sintering temperature is set at 2000 °C. The study analyzes eight case studies, involving a base case, an insulating case, and six cases with various thicknesses and heights, to evaluate the effectiveness of the suggested optimization. The results show that using insulation on the die surface reduces heat transfer from the die surface significantly, which leads to a 63% decrease in input power consumption when compared to the basic scenario. Based on a correlation study between energy and electricity, increasing die thickness raises the cross-sectional area of the electric current, which raises the amount of electric power required to attain the 2000 °C sintering temperature. The results indicate the temperature distribution in the sample is more sensitive to changes in die height than to changes in die thickness.
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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 | 0.000 | 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".