Effect of Grain Size on the Corrosion Resistance of Friction Stir Welded Mg Alloy AZ31B Joints
Bibliographic record
Abstract
The objective of this study was to clarify the effect of grain size on the corrosion rate across friction stir welded AZ31B joints when exposed in aqueous NaCl solutions. Grain size variations were achieved by preparing welded joints using AZ31B in different tempers. Single potentiodynamic polarization measurements made on the isolated base metal and stir zone in 0.6 M NaCl solution revealed no significant difference between the anodic kinetics, but revealed an apparent difference between the cathodic kinetics. The attempt to correlate extracted corrosion current density ( i corr ) values to grain size ( d −0.5 ) revealed that the slope coefficient was insignificantly different from zero, meaning that no meaningful correlation exists (albeit during the corrosion propagation stage). Scanning vibrating electrode technique (SVET) measurements across an intact AZ31B-O welded joint revealed that grain size had a more complex effect on the anodic and cathodic kinetics of the filiform-like corrosion exhibited in 0.86 M NaCl solution. Grain size had an effect on filament initiation as it consistently occurred on the surface of the base alloy (coarser-grained structure). In contrast, grain size had no effect on filament propagation as the anodic and cathodic kinetics were unaffected when moving from the base metal (coarser-grained surface) across the stir zone (finer-grained surface).
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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 | 0.001 |
| 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".