Grain Structure Formation and Texture Modification through Multi-Pass Friction Stir Processing in AlSi10Mg Alloy Produced by Laser Powder Bed Fusion
Bibliographic record
Abstract
A new strategy is proposed to modify the grain structure and crystallographic texture of laser-powder bed fusion AlSi10Mg alloy using multi-pass friction stir processing (FSP). Accordingly, 1-3 passes of FSP with 100% overlap were performed. Scanning electron microscopy and electron backscattered diffraction were used for microstructural characterization. Continuous dynamic recrystallization and geometric dynamic recrystallization are the governing mechanisms of grain refinement during FSP. The stir zones have bimodal grain structures containing large and fine grains. The multi-pass FSP caused a considerable increase in the volume fraction of the large-grained area in the stir zone, which contained higher values of low-angle boundaries and sharp shear texture components of B(11¯2)[110] and B¯(1¯12¯)[1¯1¯0]. The formation of low-energy grain boundaries in the stir zone and alignment of the low-energy crystallographic planes with the surface of the sample made the strategy of using multi-pass FSP a promising candidate for corrosion resistance enhancement in future studies. Moreover, the detailed evolution of the grains, texture components, grain boundaries, and Si particles is discussed.
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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) | 0.000 | 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".