The influence of Ce addition on the aging response of AlSi3Mg0.5 cast conductor alloy: Evaluation of electrical conductivity, mechanical properties, and microstructure
Bibliographic record
Abstract
Cerium is known as a promising alloying element to modify the microstructure and enhance the properties of Al–Si alloys. In this study, the effect of Ce addition on the aging response of AlSi3Mg0.5 alloy was investigated by analyzing variations in microhardness and electrical conductivity during isothermal aging at 180 ℃, with particular emphasis on the peak-aging condition. The results demonstrated that Ce addition slightly increased peak hardness while delaying peak-aging time. Additionally, Ce-containing alloys exhibited higher electrical conductivity throughout the aging process. Under T5 conditions, 0.5 wt% Ce enhanced yield strength by 15% (188 to 217 MPa) and electrical conductivity by 5% (47.7 to 49.9%IACS (International Annealed Copper Standard)). In contrast, under T6 conditions, the same Ce addition reduced yield strength by 5% (283 to 273 MPa) but improved conductivity by 3% (46.7 to 48.0%IACS). Differential scanning calorimetry (DSC) and transmission electron microscopy (TEM) were used to investigate the precipitation behavior of the alloys. The results indicated that Ce addition increased the activation energy for the formation of β'' and β'/B' phases while decreasing it for Si precipitates. Consequently, the β'' and β'/B' peaks in the DSC heat flow curves shifted to higher temperatures, whereas the Si peak shifted to a lower temperature. TEM further revealed that Ce reduced the number density of β'' and β'/B' precipitates by 48% and 45% under T5 and by 32% and 58% under T6 conditions, respectively. Conversely, the number density of Si precipitates increased by 20% under T5, and for T6, increased from almost zero to 162.24 μm -3 . The combined effects of these precipitates and other microstructural features on strength and electrical conductivity were quantitatively analyzed using strengthening models and Matthiessen’s rule.
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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 | 0.002 | 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) | 0.000 | 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".