Effect of Si content on microstructure, mechanical, and thermal/electrical conductivities of Al-xSi-0.3Mn-0.3Mg-0.14Fe alloy prepared by super-slow-speed die-casting
Bibliographic record
Abstract
In this study, Al- x Si-0.3Mn-0.3Mg-0.14Fe alloys ( x =6.5, 7.5, 8.5, wt.%) were prepared by super-slow-speed die-casting, and the effect of Si content on the microstructure, mechanical and thermal/ electrical conductivities in as-cast, T5, and T6 states (DIN EN 1706:2020) were investigated. It is found that the increase of Si content in the alloy enhances the formation of eutectic segregation band in the casting surface microstructure. Within the Si content range of 6.5%–8.5%, as a comprehensive evaluation criterion of mechanical properties, the quality index (QI) of 376.1 MPa can be obtained in the as-cast state of the alloy with about 7.5% Si content, 373.4 MPa in T5 state of the alloy with 6.5% Si content, and 432.2 MPa in T6 state of the alloy containing 8.5% Si. The heat treatment state significantly affects the thermal conductivity and electrical conductivity of the alloys. The eutectic silicon in the alloy is segemented and further spheroidizaed during the solution process, and the solute atoms of Mg and Si are more adequately precipitated during the aging process. Both of these greatly reduces the probability of electron scattering. Thus, T6 treatment significantly improves the electrical and thermal conductivity. With the increase of Si content, both thermal conductivity and electrical conductivity decrease slightly, demonstrating a strong correlation with the Si content in the alloy.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".