Influence of hard inclusions on microstructural characteristics and textural components during dissimilar friction-stir welding of an PM Al–Al <sub>2</sub> O <sub>3</sub> –SiC hybrid nanocomposite with AA1050 alloy
Bibliographic record
Abstract
Owing to the advantages of nanocomposites for structural applications, we present microstructural evolutions and texture development during dissimilar friction stir welding (DFSW) of an Al-matrix hybrid nanocomposite (Al-2 vol.-% Al 2 O 3 -2 vol.-% SiC) with AA1050. It is shown that DFSW can successfully be performed at a rotating speed of 1200 rev min −1 and a transverse speed of 50 mm min −1 while locating the nanocomposite at retreating side. Formation of macro-, micro-, and nano-mechanical interlocks between dissimilar base materials (BMs) as a result of FSW tool stirring action possessed an impact influence on the mechanical performance of dissimilar welds. Electron microscopy revealed formation of a three-modal grain structure from microscale (>1 µm) to nanoscale (<100 nm) range in the stir zone of the joint materials. Texture components included a mixture of [Formula: see text] shear elements and ideal [Formula: see text] random orientations, as compared to the completely random and Cu-P preferred textures for the aluminum and composite BMs.
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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".