Development of Sr modified Al–Si–Mg–Fe based alloys for automotive components
Bibliographic record
Abstract
The aim of this work is to create a comprehensive database relating to the tensile properties of a eutectic Al–Si–Mg alloy. The studies reported herein cover the dissolution of the β-iron Al 5 FeSi phase by evaluating the effect of the iron (Fe) content, the influence of modification by strontium (Sr), and the duration of the solutionizing treatment from 0 to 200 h at 540 °C. The last step is to establish a link between the tensile properties obtained and the characteristics of the microstructures, mainly the length of the β-Al 5 FeSi platelets. Solution treatment at 540 °C was applied to the eutectic alloys for times of up to 200 h. Unmodified and modified Al–Si–Mg alloys with high Fe content accelerate the dissolution of β-Al 5 FeSi; this being due to the rejection of silicon (Si) atoms towards aluminum (Al) and resulting in transforming the β-Al 5 FeSi into Al 6 Fe. The unmodified alloy shows a maximum reduction in the length of the β-phase platelets after 30 h of solution treatment, compared to 10 h for the modified alloy. Therefore, Sr addition decreases the duration of treatment due to the initial fragmentation of platelets. The process of fragmentation/dissolution of the β-Al 5 FeSi phase during solution treatment at 540 °C is associated with the ductile Al matrix characterized by the formation of dimple structure. The lack of an age hardening response of the ternary Al–12%Si-0.045%Sr alloy results in low alloy strength, making this alloy unsuitable for automotive components that may be exposed to high temperatures. The results of this study were supported by extensive tensile testing (about 1000 tensile bars). The quality-index method was found to be useful in classifying the alloys according to their performance.
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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 | 0.001 | 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".