Characterisation of microstructure and texture in friction stir welded joints of 5754 and 5182 aluminium alloy sheets
Bibliographic record
Abstract
The microstructure and texture of friction stir welded joints of hot mill stock 5754 and 5182 aluminium alloy sheets were examined after a commercial joining process. The as received sheet was cut into two pieces, one piece was cold rolled with 20 reduction in thickness, whereas the other was subsequently aged at 230C for 100 h after the 20 prestrain. These two pieces with different thermal pretreatments were joined by friction stir welding FSW. On FSW, a uniform fine grained microstructure in the through thickness of the sheet having a width corresponding to the diameter of tool shoulder was observed in both the 5754 and 5182 joints. Moreover, the microhardness profile did not reveal a softened heat affected zone between the weld and the base metal. An X-ray pole figure study showed that the 5754 and 5182 weld zones were not highly textured and both comprised of similar texture components. The calculated average sheet formability of the weld zones predicts a more isotropic behaviour than that for the start materials. Orientation image mapping of the weld zone in 5182 and semi-automatic electron backscatter pattern linescan analysis of the 5754 weld zone showed that most of the grain boundaries were high angle ones. This determination affirms that the weld zones in AA 5754 and AA 5182 joints undergo homogeneous dynamic recrystallisation throughout the thickness resulting in uniform mechanical properties.
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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".