Reactive friction stir processing of AA 5052–TiO<sub>2</sub> nanocomposite: process–microstructure–mechanical characteristics
Bibliographic record
Abstract
Friction stir processing (FSP) is a solid state route with a capacity of preparing fine grained nanocomposites from metal sheets. In this work, we employed this process to finely distribute TiO2 nanoparticles throughout an Al–Mg alloy, aiming to enhance mechanical properties. Titanium dioxide particles (30 nm) were preplaced into grooves machined in the middle of the aluminium alloy sheet and multipass FSP was afforded. This process refined the grain structure of the aluminium alloy, distributed the hard nanoparticles in the matrix and promoted solid state chemical reactions at the interfaces of the metal/ceramic particles. Detailed optical and electron microscopic studies showed that the microstructural homogeneity was improved with repetition of FSP up to four passes. The average grain size of the nanocomposite was ∼2 μm, while nanometric MgO and Al3Ti particles were formed in situ and homogenously distributed in the metal matrix. Mechanical characterisations showed that the yield strength and elongation were increased from 93±5 MPa and 13·8 to 117±3 MPa and 25·3 after employing four-pass FSP. Fractographic studies also revealed that agglomerated TiO2 particles could operate as sites of crack initiation and propagation, which led to brittle fracture. By increasing the number of FSP passes, the agglomerates were disappeared and the ductility was enhanced remarkably.
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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".