Enhanced Elevated-Temperature Strength and Creep Resistance of Dispersion-Strengthened Al-Mg-Si-Mn AA6082 Alloys through Modified Processing Route
Bibliographic record
Abstract
In the present work, we investigated the possibility of introducing fine and densely distributed α-Al(MnFe)Si dispersoids into the microstructure of extruded Al-Mg-Si-Mn AA6082 alloys containing 0.5 and 1 wt % Mn through tailoring the processing route as well as their effects on room- and elevated-temperature strength and creep resistance. The results show that the fine dispersoids formed during low-temperature homogenization experienced less coarsening when subsequently extruded at 350 °C than when subjected to a more typical high-temperature extrusion at 500 °C. After aging, a significant strengthening effect was produced by β″ precipitates in all conditions studied. Fine dispersoids offered complimentary strengthening, further enhancing the room-temperature compressive yield strength by up to 72–77 MPa (≈28%) relative to the alloy with coarse dispersoids. During thermal exposure at 300 °C for 100 h, β″ precipitates transformed into undesirable β-Mg2Si, while thermally stable dispersoids provided the predominant elevated-temperature strengthening effect. Compared to the base case with coarse dispersoids, fine and densely distributed dispersoids with the new processing route more than doubled the yield strength at 300 °C. In addition, finer dispersoids obtained by extrusion at 350 °C improved the yield strength at 300 °C by 17% compared to that at 500 °C. The creep resistance at 300 °C was greatly improved by an order of magnitude from the coarse dispersoid condition to one containing fine and densely distributed dispersoids, highlighting the high efficacy of the new processing route in enhancing the elevated-temperature properties of extruded Al-Mg-Si-Mn alloys.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".