Surface properties of friction stir welded dissimilar joints of AA7075 and Mg-WE43 alloys: Effect of positional arrangement
Bibliographic record
Abstract
• The average grain size of the al/mg joint is reduced from 4.11 to 2.04 µm. • The wear rate decreased from 0.41 mm³/Nm to 0.27 mm³/Nm. • The corrosion resistance is improved from 77.59 to 56.299 mpy. • The reduction in average microhardness was achieved by reduced IMC formation. This study investigates the surface properties of the dissimilar AA7075/Mg-WE43 joints wherein the effect of positioning the AA7075 on the advancing or retreating sides during friction stir welding (FSW) was scrutinized. The EBSD analysis revealed that the average grain size reduced from 4.11 ± 0.7 µm to 2.04 ± 0.9 µm when AA7075 was shifted from advancing to retreating side. The results showed that positioning AA7075 on the retreating side significantly reduced the wear rate from 0.41 mm³/Nm to 0.27 mm³/Nm and mitigates the problem of brittle intermetallic compound (IMC) formation, consequently reducing the average microhardness. Corrosion resistance improved from 77.59 mpy to 56.299 mpy. The higher grain refinement improved wear resistance due to higher grain boundary density and greater chances of formation of protective oxide films. The current work thus enhances the practical applicability of AA7075/Mg-WE43 welds for lightweighting of automotive structures.
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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".