The effect of SiC/Al<sub>2</sub>O<sub>3</sub> particles used during FSP on mechanical properties of AZ91 magnesium alloy
Bibliographic record
Abstract
Abstract Friction stir processing, as a method of changing the properties of a metal, through intense, localized plastic deformation, has been developed based on the basic principles of friction stir welding. In the current research, mechanical properties of AZ91 magnesium alloy were modified by the application of friction stir processing. Surface composites using SiC as well as Al 2 O 3 nano-particles were developed. Yield and tensile strengths as well as hardness values of specimens were measured. The results showed that friction stir processing modified the size of grains noticeably and improved the mechanical properties. The results also indicated that SiC particles were more beneficial than Al 2 O 3 particles with regard to improvement of the properties. It was also concluded that homogeneous distribution of particles resulted in the decrease of grain sizes to 3 μm and the increase in strength and formability index to 390 MPa and 6 500 MPa, respectively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".