The Effects of Iron-Bearing Intermetallics on the Fitness-for-Service Performance of a Rare-Earth-Modified A356 Alloy for Next Generation Automotive Powertrains
Bibliographic record
Abstract
Aimed at improving the tensile strength and creep resistance of a rare earth-modified A356 alloy, this study adjusted the Mg and Mn concentration in the alloy, specifically aiming to transform the harmful Al5FeSi and Al9FeSi3Mg5 phase into Al15(Fe,Mn)3Si2. It was found that lowering the Mg concentration from 0.49 to 0.25 wt.% and raising the Mn concentration from 0.10 to 0.41 wt.% resulted in a near complete transformation of the Fe-bearing phases. This transformation led to a greater total volume fraction of Fe-intermetallics (2.9 to 4.1%), without affecting the volume fraction of the desirable, temperature-resistant, AlSiRE phase. Moreover, the chemistry modification led to a shift in the morphology of the AlSiRE phase while reducing its size. Combined with the decreased volume fraction of the harmful Fe precipitates, the chemistry modification improved the yield strength (YS), ultimate tensile strength (UTS) and modulus of elasticity by ~14%, 9%, and 10%, respectively. In addition, the steady-state creep rates of the high Mn alloy were lower at all stresses as compared to the low Mn alloy and the fracture stress was ~15 MPa higher, reaching 100% of the alloy’s original 250 °C YS.
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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".