Study of Mechanical and Tribological Behavior of Aluminium Metal Matrix Composite Reinforced with Alumina
Bibliographic record
Abstract
Aluminium alloy has gained importance in the automotive and aerospace industry as it is easily available and easy in manufacturing. In the recent years, materials science has gained huge importance in the field of composites. In the field of composites metal matrix composite is playing a lead role in industrial applications. The unique combinations of properties provided by aluminum and its alloys make aluminum one of the most versatile, economical and attractive metallic materials. To enhance the properties of aluminum, it has been reinforced with alumina, silicon carbide, graphene and others. In this study, A357 aluminum has been strengthened by using different weight percent of alumina as reinforcement. Percentage of alumina used are 4%, 8% and 12% to enhance the mechanical and tribological property of A357.The fabricated samples were studied to understand the performance of the composite for mechanical and tribological characters. It was observed that the composites showed superior properties compared to the base material. Statistical analysis i.e. regression analysis has been carried out for hardness and tensile strength of alumina reinforced aluminum composite.
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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".