Numerical optimization of sample and die geometric parameters to increase the attainable temperature during spark plasma sintering of TiC ceramics
Bibliographic record
Abstract
The present study offers a comprehensive thermal modeling of spark plasma sintering (SPS) for a titanium carbide (TiC) sample. Utilizing COMSOL Multiphysics Software, the research investigates the temperature distribution within the TiC sample, situated within a graphite die. The study employs governing equations for heat diffusion, augmented by terms accounting for Joule heating, to calculate temperature variations. Boundary conditions, particularly at the upper and lower limits of the system, are explicitly accounted for, with cooling mechanisms modeled as convection. Through the application of the Taguchi method and Analysis of Variance (ANOVA), the study identifies the diameter of the sintering sample as the most significant parameter affecting the maximum temperature at the center of the TiC sample, with a significance of about 87%. The outer diameter of the graphite die followed with a significance of slightly more than 10%, and the thickness of the TiC sample had a significance of around 2%. The findings contribute to a nuanced understanding of the SPS process, offering valuable insights for optimizing the sintering parameters. Numerical results further underscore the importance of specific geometric parameters in the SPS process. This study serves as a robust foundation for future research aimed at refining the SPS process for TiC samples and other materials.
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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 | 0.001 | 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 | 0.001 | 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".