The Role of Mg Content and Aging Treatment on the Tensile and Fatigue Properties of Die-Cast 380 Alloy
Bibliographic record
Abstract
The main objective of this contribution was to determine the impact of magnesium (Mg) concentration and solidification rate (about 800 °C/s) on the mechanical properties of commercial A380.1 die-cast alloy. Respective amounts of 0.10%, 0.30%, and 0.50% Mg were used to establish their influence on the main tensile properties, namely, the ultimate limit, the elastic limit, and the percentage of elongation to fracture. The study also focused on the effect of magnesium on the fatigue behavior of A380.1 alloy where the role of surface defects and internal defects (porosity, oxide films, and inclusions) on the alloy fatigue life was also determined. The tensile properties were analyzed in order to optimize the heat treatments of T6 (under-aging) and T7 (over-aging). Consequently, the influence of several parameters was evaluated using tensile testing and optical and scanning electron micrography. Fatigue strength was investigated by performing rotational bending tests. The results show that the alloy tensile strength parameters improve with up to 0.3% Mg. Further addition of Mg, i.e., 0.5%, does not produce any significant improvement with respect to either traction or fatigue. It is observed that the tensile properties fluctuate according to the Guinier-Preston zones which occur during heat treatment, while the fatigue properties decrease as the Mg content increases. In contrast to a mechanical fatigue failure mechanism, in the present study, cracks were initiated at the sample's outer surface and then propagated toward the center.
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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) | 0.000 | 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".