Synergistic impact of tool geometry and heat input on microstructure and texture development in friction stir processed AA6061-Graphene nanocomposites
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it