Effect of mischmetal concentration and superheating on the microstructure and tensile properties A319.1: Role of Fe content
Bibliographic record
Abstract
The aim of this research is to study the effect of certain metallurgical parameters such as the addition of a mixture of rare earths (mischmetal) and superheating on the microstructure and tensile properties of the Al–Si–Cu alloy A319.2 containing 0.4%, 0.8% and 1.2% iron used in the automotive industry. The mischmetal (MM) precipitates in the form of platelets like the β-Al 5 FeSi leading to a marked refinement in eutectic Si particles at 5% MM. In addition, α- Al 15 (Fe,Mn) 3 Si 2 . segregated particles and mischmetal-bound intermetallics persist in the microstructure even after T6 heat treatment. The tensile properties of 0.8% iron alloys deteriorate when the mischmetal concentration and superheat temperature increase, although the silicon phase undergoes significant changes. Alloys with a high iron concentration (1.2% Fe) showed a slight decrease in the length of the β-Al 5 FeSi phase platelets and of the mischmetal compared to alloys with 0.4% and 0.8% Fe when the concentration of mischmetal increases to 5% and when the superheating temperature reaches 950 °C. This observation explains the increase in the % elongation to fracture of the 1.2% Fe-5% mischmetal alloy cast directly from 750 °C or after superheating at 950 °C, followed by in-furnace cooling to 750 °C. The ultimate tensile strength and yield strength degrade with mischmetal, but improve slightly with superheating; only for the 1.2% Fe-0% mischmetal alloy, its yield strength decreases once superheated to 950 °C.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".