The Influence of Cu Addition on Dispersoid Formation and Mechanical Properties of Al-Mn-Mg 3004 Alloy
Bibliographic record
Abstract
The effect of Cu addition on dispersoid precipitation, mechanical properties and creep resistance was investigated in an Al-Mn-Mg 3004 alloy. The addition of Cu promoted dispersoid precipitation by increasing the number density and decreasing the size of dispersoids. Metastable β′-Mg2Si and Q-AlCuMgSi precipitates were observed during the heating process and both could provide favorable nucleation sites for dispersoid precipitation. The addition of Cu improved the thermal stability of dispersoids during a long-term thermal holding at 350 °C for 500 h. Results of mechanical testing show that the addition of Cu remarkably improved the hardness at room temperature, as well as the yield strength and creep resistance at 300 °C, which was mainly attributed to dispersoid strengthening and Cu solid solution strengthening. The yield strength contribution at 300 °C was quantitatively evaluated based on the dispersoid, solid solution and matrix contributions. It was confirmed that dispersoid strengthening is the main strengthening mechanism in the experimental alloys.
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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) | 0.000 | 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 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".