Influence of Cooling Rate on Microsegregation Behavior of Magnesium Alloys
Bibliographic record
Abstract
The effect of cooling rate on microstructure and microsegregation of three commercially important magnesium alloys was investigated using Wedge (V-shaped) castings of AZ91D, AM60B, and AE44 alloys. Thermocouples were distributed to measure the cooling rate at six different locations of the wedge casts. Solute redistribution profiles were drawn based on the chemical composition analysis obtained by EDS/WDS analysis. Microstructural and morphological features such as dendrite arm spacing and secondary phase particle size were analyzed using both optical and scanning electron microscopes. Dendritic arm spacing and secondary phase particle size showed an increasing trend with decreasing cooling rate for the three alloys. Area percentage of secondary phase particles decreased with decreasing cooling rate for AE44 alloy. The trend was different for AZ91D and AM60B alloys, for both alloys, area percentage ofβ-Mg17Al12increased with decreasing cooling rate up to location 4 and then decreased slightly. The tendency for microsegregation was more severe at slower cooling rates, possibly due to prolonged back diffusion. At slower cooling rate, the minimum concentration of aluminum at the dendritic core was lower compared to faster cooled locations. The segregation deviation parameter and the partition coefficient were calculated from the experimentally obtained data.
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".