Improving the elevated-temperature mechanical properties of AA3004 hot-rolled sheets by microalloying with Mo and optimizing the process route
Bibliographic record
Abstract
The present work investigated the influence of Mo addition and thermomechanical process routes on microstructural evolution and elevated-temperature mechanical properties of Al–Mn–Mg 3004 alloys. Various combinations of heat treatment and hot rolling were applied to fabricate hot-rolled sheets. The results revealed that microalloying with Mo and two-step heat treatment increased the number density and volume fraction of dispersoids and decreased the volume fractions of dispersoid-free zones. The different processing routes had important impacts on microstructural evolution. The alloys processed with heat treatment followed by hot rolling had finer and better distributions of dispersoids than those subjected to hot rolling prior to heat treatment. The former resulted in higher tensile strengths at room and elevated temperatures. Among all conditions, the Mo-containing alloy subjected to two-step heat treatment followed by hot rolling exhibited the highest elevated-temperature properties and reached a yield strength of 93 MPa at 300 °C. Both the base and Mo-containing alloys subjected to two-step heat treatment followed by hot rolling showed excellent thermal stabilities up to 350 °C and almost no significant change in yield strengths after thermal exposure at 300–350 °C for 100 h.
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".