Electron backscattered diffraction analysis of friction stir processed nanocomposites produced via spark plasma sintering
Bibliographic record
Abstract
Summary In the present study, Spark Plasma Sintered (SPSed) aluminium matrix composites were severely deformed through Friction stir processing (FSP). Pure aluminium powders and bimodal sized Al2O3 particles (80 nm and 25 m) were firstly mixed by ball milling and then consolidated by spark plasma sintering. The effect of the heat input as well the bimodal particle size of the alumina on the materials’ microstructure and texture development was evaluated by electron back scattered diffraction (EBSD) analysis. The EBSD analysis clearly showed that the SPSed nanocomposites possessed bimodal aluminium matrix grain structure as well as a crystallography characterised by random texture. In addition, microstructural examination revealed that the partial recrystallisation occurred during SPS for all the nanocomposites. Also, it is revealed that the Zener pinning effect of Al2O3 nanoparticles retarded recrystallised grain growth following recrystallisation during FSP and then leading to grain refinement of the aluminium. The results revealed that the heat generated during FSP has a remarkable effect on the grain distribution as well as on the crystallographic orientation. Also, a mixture of {112} <110> shear elements and an ideal strong B/ component were observed. The microstructural changes, occurred during FSP in the stir zone region for Al‐Al2O3 nanocomposites, were attributed to both the discontinuous along with the continuous recrystallisation (DDRX/CDRX). It should be pointed out that with increasing the heat input, recrystallised grains portion increased.
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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) | 0.002 | 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".