Effect of C and Al Elements on High Specific Resistance and High Rigidity of Ultra-High Strength TiC<sub>(1−<i>X</i>)</sub>/Ti Metal Matrix Composites Fabricated by Blended Elemental Reactive Sintering
Bibliographic record
Abstract
The effects of C and Al concentrations on the specific resistance and rigidity of newly developed ultra-high strength titanium-based metal matrix composites (Ti-MMCs), fabricated using blended elemental reactive sintering (BERS), were investigated. Both TiC(1−X)/Ti–6Al–4V and Ti–8.6Al–5.7V composites were compared with TiB/, SiC/ and AlN/Ti-MMCs. TiC was found to react with Ti powder and to transform to TiC(0.50–0.62) during sintering. The resulting TiC(0.50–0.62)/Ti–8.6Al–5.7V exhibited a specific resistance of 2.33 µΩm, a Young’s modulus of 135 GPa and a tensile strength of 1.25 GPa, with a substantial elongation of approximately 2.5%. In contrast, TiB/Ti–6Al–4V showed excellent mechanical properties but an extremely low electrical resistance because the conducting TiB particles had a specific resistance of only 0.07 µΩm. Both SiC and AlN also reacted with Ti powder during sintering to form a brittle phase at the interfaces between the particles and the Ti matrix. As a result, Ti–6Al–4V MMCs suitable for use as structural materials could not be fabricated using BERS with SiC or AlN. The high specific resistance of the TiC(0.50–0.62)/Ti–8.6Al–5.7V is partly attributed to the C deficiency of the TiC(0.50–0.62) particles, which results in a specific resistance of approximately 1.7 µΩm. This value is approximately three times higher than the value of 0.52 µΩm for stoichiometric TiC particles. The solubility of excess C and Al in the Ti matrix also increases the specific resistance of the material.
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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".