Influence of CNTs decomposition during reactive friction‐stir processing of an Al–Mg alloy on the correlation between microstructural characteristics and microtextural components
Bibliographic record
Abstract
Summary Multipass friction‐stir processing was employed to uniformly disperse multiwalled carbon nanotubes (MW‐CNTs) within an Al–Mg alloy metal matrix. Decomposition of MW‐CNTs occurs in situ as a result of solid‐state chemical reactions, forming fullerene (C60) and aluminium carbide (Al4C3) phases during reactive high temperature severe plastic processing. The effects of this decomposition on the microstructural features, dynamic restoration mechanisms and crystallographic microtextural developments are studied for the first time by using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM) analysis. The formation of an equiaxed grain structure with an average size of ∼1.5 μm occurs within the stirred zone (SZ) under the influence of inclusions which hinder grain boundary migration via Zener‐Smith pinning mechanisms during the discontinuous dynamic recrystallisation (DDRX). Formation of two strong Cubic and Brass microtextural components in the heat affected zone (HAZ) and thermomechanical affected zone (TMAZ) was noted as compared to the completely random and Cube components found in the base and SZ regions, respectively. The microstructural modification led to hardening and tensile strength improvement for the processed nanocomposite by ∼55% and 110%, respectively with respect to the annealed Al–Mg base alloy.
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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".