Investigation of Dry Tribo-Behavior of Aluminum Alloy AA6061/Al2O3/Graphite Composites Synthesized by Stir Casting Technique
Bibliographic record
Abstract
In order to meet the requirements of lightweight, high strength, and wear resistance, metal matrix composites are being manufactured with increasing interest, according to recent advancements in material science. Alumina (Al2O3) and Graphite (Gr) were added to the aluminum alloy matrix (AA6061) to increase the dry sliding wear resistance and to explore the impact of normal applied load and sliding speed on the coefficient of friction as well as its wear behavior. The composites were made by the stir casting technique. Hardness and tensile strength were two examples of mechanical qualities that have been assessed. Compared to the unreinforced aluminum alloy, the tensile strength of the metal matrix composites was improved by 17.7%, and the hardness increased by 44.1% at a loading of AA6061 (90wt%)/ Al2O3 (5wt%)/Gr (5wt%). A pin-on-disc tribometer was utilized to analyze the dry sliding tribo-behaviors of AA 6061 alloy reinforced with Al2O3 and Gr by varying the applied load and the sliding distance. The wear resistance of aluminum alloy reinforced with Gr (5wt%)/Al2O3(5wt%) increased by 85.0% when the load was applied at 25N compared to unreinforced aluminum alloy, and the frictional coefficient decreased by 58.0%. Additionally, an optical microscope and scanning electronic microscope were used to assess the worn surfaces.
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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 | 0.001 | 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".