An EBSD study on microstructure and texture development in graphene-reinforced Al–Mg–Si nanocomposites via FSP
Bibliographic record
Abstract
This study investigates the microstructure evolution and texture development of friction stir processed (FSP) AA6061-T6 Al–Mg–Si matrix composites reinforced with graphene nanoplatelets . Using electron backscatter diffraction (EBSD), we studied changes in grain boundary characteristics and texture components. As heat input increases, the Zener-Hollomon parameter decreases, causing grain size to grow. Particles, including those of Fe-rich and Mg 2 Si nature, also coarsen from average sizes of 0.9–1.4 μm, and 0.2–0.5 μm, respectively. Higher heat input and plastic strain lead to a reduction of the fraction of low-Σ boundaries, while increasing high-Σ boundaries suggest activation of other deformation mechanisms , i.e., from dislocation slip to twinning, respectively, as a function of dislocation generation and recovery kinetics. Grain orientation spread (GOS) and kernel average misorientation (KAM) values also decrease, indicating a higher homogeneity and smaller local disorientations under the excess heat. The higher texture indices observed in the composite samples suggest that frictional heat and graphene addition collectively enhance preferred orientations, potentially leading to higher anisotropy. Principal texture components shift from {101} < 1 ‾ 2 ‾ 1 > , { 1 ‾ 2 ‾ 3 }<634>, {111} < 1 1 ‾ 0 > , {332} < 1 ‾ 1 ‾ 3 > , {013} < 2 3 ‾ 1 > , and {214} < 1 ‾ 2 ‾ 1 > in the base metal to {011} < 1 2 ‾ 2 > , {011} < 0 1 ‾ 1 > , and {112} < 1 1 ‾ 0 > in composites. Components such as {101} < 0 1 ‾ 0 > remains unaffected.
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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".