Submerged Dissimilar Friction Stir Welding of AA6061 and AA7075 Aluminum Alloys: Microstructure Characterization and Mechanical Property
Bibliographic record
Abstract
The possibility of underwater dissimilar friction stir welding of AA6061 and AA7075 aluminum alloy was explored to overcome the problem of hardness loss in different microstructural zones. Optical microscopy and electron backscattered diffraction were employed to characterize the microstructure of the joint. Vickers hardness measurements were conducted on the cross-section of the joint to evaluate the mechanical strengths. The results showed that the microstructure of the AA7075 side had undergone the same mechanisms as those occurring during conventional friction stir welding. In the case of the AA6061 side, in addition to typical restoration mechanisms, the grain subdivision was observed. The AA7075 side had finer grains compared to the AA6061 side, which may be related to the different morphology and size of precipitates. Moreover, friction stir welding caused a reduction in the hardness values in all the microstructural areas compared to those of corresponding base materials. For example, it caused a reduction in the hardness of a thermomechanically affected zone from 105 HV to 93 HV in the AA6061 side, and from 187 HV to 172 HV in the AA7075 side. The underwater media improved the overall hardness values in thermo-mechanically affected zones (13% reduction in hardness) compared to those reported in literature (57% reduction in hardness).
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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".