Mechanical Performance of Zr‐Containing 354‐Type Al‐Si‐Cu‐Mg Cast Alloy: Role of Additions and Heat Treatment
Bibliographic record
Abstract
In this article, the volume fraction of intermetallic compounds in Zr‐containing 354‐type Al‐Si‐Cu‐Mg alloys, characteristics of eutectic Si particles, and tensile, hardness, and impact properties have been evaluated with varying Ni and Mn contents and combination. The results revealed that additions of Ni and Mn in different amounts and combinations increased the volume fraction of intermetallic compounds in the tailored alloys, compared to the base alloy (cf. 12.21% for 4% Ni‐containing alloy with 2.5% for base alloy), producing a significant effect on the mechanical performance. The proposed additions enhanced the mechanical performance of the alloys, namely, the ambient‐ and elevated‐temperature tensile properties, hardness values, and impact properties. For the Mn‐containing alloys, the improvement in properties was attributed to the formation of sludge particles in the form of blocky α‐Al15(Fe,Mn)3Si2 alongside the script‐like α‐iron phase that resisted crack propagation. The precipitation of Ni‐bearing phases such as Al9FeNi, Al3CuNi, and Al3Ni in the Ni‐containing alloys improved the mechanical properties through hindering cracks propagation. Interestingly, addition of 0.75 wt.% Mn to the base alloy proved to be competitive in strength values to the addition of 2 and 4 wt.% Ni, and better in terms of ductility values. The investigations showed that the variations in hardness and impact values follow the same trend as variations in the percentage volume fraction of intermetallic compounds, i.e., maximum property value is associated to the alloy with highest volume fraction of intermetallic compounds. Furthermore, the impact properties showed higher dependency on Al2Cu phase particles rather than the eutectic Si particles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".