Texture and grain refinement for enhanced strength and ductility in friction stir welding of cold-rolled thin-strip rapidly solidified AA5182 Al–Mg alloy
Bibliographic record
Abstract
Thin-strip (TS) casting enhances Al-Mg alloy production by rapidly solidifying strips, resulting in finer grains, higher solute supersaturation, and fewer intermetallic phases making it ideal for automotive and packaging industries. This study, for the first time, investigates the microstructural evolution and mechanical properties of cold-rolled and friction stir welded (FSWed) TS AA5182 alloy, focusing on grain refinement, texture development, and mechanical performance. Microstructural analysis reveals significant grain refinement through rolling and FSW, with dynamic recrystallization playing a key role in texture evolution. While rolling enhances strength at the expense of elongation, FSW improves both tensile strength and toughness, particularly at lower pin rotational speeds. Texture analysis indicates that lower rotational speeds result in more pronounced texture, whereas higher speeds weaken overall texture strength. It was found that the formation of grains with the orientation in the stir zone helps minimize stress concentration, promote uniform stress distribution, and reduce strain localization. Schmid factor analysis suggests that the balance of active slip systems and texture evolution contributes to the optimized mechanical performance observed in samples processed at lower rotational speeds. These findings emphasize the synergistic effects of grain refinement and texture strengthening, particularly the development of preferred orientations, in enhancing the strength and ductility of AA5182 alloy. This study offers valuable insights into the unique microstructural and mechanical transformation mechanisms in rolled and FSW samples, contributing to the optimization of TS alloys for industrial use and the development of more efficient and sustainable manufacturing techniques.
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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) | 0.001 | 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".