Synergistic impact of tool geometry and heat input on microstructure and texture development in friction stir processed AA6061-Graphene nanocomposites
Bibliographic record
Abstract
The synergistic effect of tool geometry and process heat input on the microstructure and texture development of AA6061-Graphene nanocomposites through friction stir processing (FSP) was studied. The findings reveal that for composites fabricated with a tool having a pin cone angle (PCA) of 2.5°, increased heat input leads to a pronounced strain rate effect, resulting in finer recrystallized grains (3.0 ± 0.1 µm). Conversely, for composites produced with a PCA of 2°, reduced heat input enhances the uniform dispersion of graphene particles and lowers processing temperatures, yielding finer grains (1.8 ± 0.2 µm) in the processed zone. The fraction of low-Σ boundaries, such as Σ3, decreases after FSP relative to the base metal. However, for the composite with a PCA of 2.5°, a higher fraction of low-Σ boundaries (0.64 %) is observed at minimal heat input compared to the composite processed with a PCA of 2° (0.37 %). With increasing heat input, this trend reverses, and the fraction of low-Σ boundaries in the composite processed with a PCA of 2° reaches 1.26 %, surpassing that of the 2.5° (0.18 %). As the heat input rises from 2539 to 4528 J/mm, the density of low-angle grain boundaries (LAGB) in composites processed with a PCA of 2° increases from 15.8 % to 29.9 %. In contrast, for composites with a PCA of 2.5°, the LAGB density decreases from 31.2 % to 25.0 % as the heat input rises from 2543 to 4534 J/mm. FSP with a PCA of 2.5° enhances the intensity of the Q {013}< 2–31 > texture component with increasing heat input. However, in composites processed with a PCA of 2°, the trend differs, as increased heat input promotes the dominance of Rotate-Cube {001}< 1–10 > , Q, and B {111}< 1–10 > components.
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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".