Effect of Dispersoids and Intermetallics on Hardening the Al‐Si‐Cu‐Mg Cast Alloys
Bibliographic record
Abstract
The principal aim of the present research work was to compare the role of dispersoids Al 2 O 3 (∼15 μ m average particle size) and SiC (∼15 μ m average particle size) with that offered by Zr‐ and Ni‐based intermetallics (∼35–70 μ m average particle size) on the hardening of cast aluminum 354 alloy (9.1% Si, 0.12% Fe, 1.8% Cu, 0.008% Mn, 0.6% Mg, and 87.6% Al) at ambient temperature. There is no observable poisoning effect on the refinement of grain size after the addition of Zr to the alloys investigated in this study. The tensile test results were examined in light of the microstructural features of the corresponding alloy samples. The contribution of the added dispersoids or Ni and Zr alloying elements on the tensile properties of the 354 alloys was determined employing ∆P plots (where P = Property, UTS, YS, or %El), using the base alloy (in the as‐cast condition) as a reference point. The tensile results were supported by investigating the precipitation‐hardening phases using scanning and transmission electron microscopy as well as examining the fracture surfaces of selected conditions applying field emission scanning electron microscopy. The results show that, in all cases, Al 2 Cu phase is the main hardening agent. The contribution of about 1.5 vol% of SiC or Al 2 O 3 to the strength of the base alloy is higher than that offered by Zr‐ and Ni‐based intermetallics, under the same aging treatment.
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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 | 0.000 | 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".