Precipitation Kinetics and Evaluation of the Interfacial Mobility of Precipitates in an AlSi7Cu3.5Mg0.15 Cast Alloy with Zr and V Additions
Bibliographic record
Abstract
Recent environmental restrictions constrained car manufacturers to promote cast aluminum alloys working at high temperatures (180 °C–300 °C). The development of new alloys permits the fabrication of higher-strength components in engine downsizing. Those technologies increase internal loadings and specific power and stretch current materials to their limits. Transition metals in aluminum alloys are good candidates to improve physical, mechanical, and thermodynamic properties with the aim of increasing service life of parts. This study is focused on the modified AlSi7Cu3.5Mg0.15 alloy where Mn, Zr, and V have been added as alloying elements for high-temperature applications. The characterization of the cast alloy in this study helps to evaluate and understand its performance according to their physical state: As-cast, as-quenched, or artificially aged. The precipitation kinetics of the AlSi7Cu3.5Mg0.15 (Mn, Zr, V) alloy has been characterized by differential scanning calorimetry (DSC), transmission electron microscopy (TEM) observations, and micro-hardness testing. The Kissinger analysis was applied to extract activation energies from non-isothermal DSC runs conducted at different stationary heating rates. Finally, first-order evaluations of the interfacial mobility of precipitates were obtained.
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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".