Effect of Graphite on Mechanical and Tribological Properties of Al6061/SiC Hybrid Composites
Bibliographic record
Abstract
This study investigates the effects of incorporating graphite into a Al6061/SiC hybrid metal matrix composite on its mechanical and wear properties.The composites are fabricated using a stir casting technique, with SiC and Graphite particles added in different weight percentages ranging from 2-8%.Mechanical properties such as hardness, tensile strength, flexural strength, and compressive strength are evaluated.Results show that the composite with 6 wt.% of hybrid reinforcement exhibits significant improvement in hardness (30%), tensile strength (10.82%), compressive strength (68.14%), and flexural strength (85%) compared to pure Al6061 alloy.Furthermore, a wear test is performed using a pin-on-disc machine under dry conditions to assess the influence of parameters on the wear rate and coefficient of friction (COF).Tests are conducted at various loads (1-3 kgf), sliding speeds (150-450 rpm), and sliding distances (1000-2000 m).Among all reinforcements, the composite with 6% hybrid reinforcement exhibits the lowest wear rate and COF.Overall, this study provides valuable insights into the mechanical and wear properties of Al6061/SiC/graphite hybrid composites and highlights their potential for various industrial applications.These findings may pave the way for further research in the field of metal matrix composites and their applications.
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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".