Effects of heat treatment and testing temperature on the tensile properties of Al–Cu and Al–Cu–Si based alloys
Bibliographic record
Abstract
Abstract The present study aimed at investigating the effects of different additives and heat treatments on the mechanical properties of Al-2.4 %Cu-1.2 %Si-0.4 %Mg-0.4 %Fe-0.6 %Mn alloy, a casting alloy intended for automotive applications. The research was accomplished through a study of the tensile properties in both as-cast and heat-treated conditions, where the effects of different heat treatments, i. e., T5, T6, T62 and T7, commonly applied to aluminum casting alloys were evaluated at ambient and at high temperature (250 °C), using different holding (stabilization) times at testing temperature. Six alloys were prepared using 0.15 wt.% Ti grain-refined alloy – considered as the base alloy B0, and alloys B1 and B2, and D0, D1, and D2 containing various amounts of Ni, Cr, V, Zr and La, added individually or in combination. The D-series alloys had a higher Si content of 8 wt.%. The results showed that T6 and T62 treatments provide the best improvements in strength at room temperature. At high temperature, the tensile properties vary depending on the stabilization time and heat treatment. The best alloy quality is provided by B1 and D1 alloys in T62 (444/368 MPa) and T7 (430/360 MPa) conditions, respectively.
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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 | 0.001 |
| 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".