Effect of Cooling Rate on AlN Precipitation in FeCrAl Stainless Steel During Solidification
Bibliographic record
Abstract
The effect of cooling rate on the evolution of AlN inclusions precipitated during solidification in FeCrAl stainless steel was investigated using an experimental study and thermodynamic and kinetic calculations. The number and size of AlN inclusions precipitated under different cooling rates were examined with the feature function of the field-emission scanning electron microscope. A model combining micro-segregation and the diffusion-controlled growth model was set up to determine the mechanism of AlN particle growth. The results showed that AlN precipitates in the mushy zone. The size of AlN particles decreases and the number of AlN particles increases with increasing cooling rate, whereas the volume fraction is essentially unchanged. The AlN particles grow during solidification after the content of solutes in molten steel has exceeded the concentration in equilibrium with AlN. The nitrogen content varies significantly with the cooling rate during solidification. Increasing the cooling rate and reducing the nitrogen content in the molten steel can reduce the AlN particle size in FeCrAl alloys as the growth time decreases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".