Effect of Mg Content and Heat Treatment on the Mechanical Properties of Low Pressure Die-Cast 380 Alloy
Bibliographic record
Abstract
The present study was carried out on a 380 alloy containing 9.13% Si, 3.22% Cu, 1.01% Fe, 0.06% Mg, 0.16% Mn, and 2.28% Zn. The magnesium level was increased to 0.3 and 0.55%, by adding pure Mg to the melt. Tensile and fatigue samples were produced using low pressure die casting. The results show that the average dendrite arm spacing was about 6 μm. Increasing the amount of Mg from 0.06% to 0.55% increased the volume fraction ofπ-Al8Mg3FeSi6and Q-Al5Cu2Mg8Si6phases from 0.8% to 1.7%. Following solutionizing at 490°C for 8 h, the maximum ultimate tensile strength was obtained from alloys containing 0.3% Mg. Further increases in Mg content resulted in an increase in the amount of insoluble intermetallics and, hence, low tensile strength. Aging at 155°C for times up to 25 h resulted in a linear increase in the alloy strength regardless of the amount of added Mg. Aging at 220°C, however, revealed multiple peaks corresponding to the precipitation of various phases. A good relation between the applied force and the number of cycles prior to failure was established. The alloy containing 0.3% Mg produced the best fatigue resistance. The effect of porosity was more pronounced on the fatigue samples than on the tensile bars.
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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".