Effect of Nanosized TiO<sub>2</sub> on Nucleation and Growth of Cristobalite in Sintered Fused Silica Cores for Investment Casting
Bibliographic record
Abstract
Sintered fused silica is often used for making sacrificial cores in investment castings of Ni superalloys. Their usage is fundamental in the manufacture of precise superalloy gas turbine components with complex internal cooling passages. In this study SiO2/ZrSiO4/TiO2 cores were prepared from fused silica powders with different grain size and zircon and TiO2 content by slip casting method. Green samples were sintered at 1230°C at various soaking time: from 0,5 to 10 hours. Thermomechanical and microstructural properties of optimized silica obtained by add of 1,5%wt of TiO2 to SiO2/ZrSiO4 composition have been investigated by three point bending tests, XRD and Hg porosimetric analysis. The influence of cristobalite content on thermal stability at high temperature was studied by an optical dilatometer. At temperature below 1200°C TiO2 appears to act as a phase transformation inhibitor reducing the transformation rate of fused silica to cristobalite at high temperatures. At higher temperature it speeds up the formation of cristobalite. A comparison with commercial silica cores made by injection moulding has been performed. A prototype core was obtained and an investment casting was performed on that.
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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 | 0.000 |
| 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".