Effect of Cu Content on the Alloy Tensile Properties of Al-Cu Based Alloys Tested at 25 °C and 250 °C: Application of the Concept of Quality Index
Bibliographic record
Abstract
The present work was performed on three versions of a newly developed alloy coded T200 containing 6.5% Cu, 0.1% Fe, 0.45% Mg, and 0.18% Zr in addition to A319 and A356 alloys (grain refined and Sr-modified). Tensile bars were subjected to 13 different heat treatments prior to testing at either 25 °C or 250 °C. The tensile data were analyzed using the quality index method. The results obtained showed that, due to the high copper content in the T200 alloy coupled with proper grain refining, the alloy possesses the highest quality as well as improved resistance to softening when tested at 250 °C among the five alloys. The results also demonstrate the best heat treatment condition to maximize the use of the T200 alloy for automotive applications. Grain-refined alloy B, treated in the T6 temper and tested at 250 °C, exhibited the best combination of the four tensile parameters, i.e., UTS, YS, %El, and Q-values: 308 MPa, 304 MPa, 2.3%, and 352 MPa, respectively, which are comparable with those obtained from the 356 alloy: 309 MPa, 305 MPa, 2.8%, and 375 MPa in the same order.
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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".