Effect of Graphite Addition on Aluminum Hybrid Matrix Composite by Powder Metallurgy Method
Bibliographic record
Abstract
The requirement for optimal strength and lightweight materials is a challenge in today's automotive industry. In this study, the synthesis of aluminum matrix hybrid composites containing magnesium and copper with the addition of graphite 0, 1, 3, and 5% by volume was carried out using powder metallurgy techniques and investigated the effect of the treatment given. The investigation of composite properties was carried out by testing density, porosity, hardness, wear rate, XRD, and SEM investigations integrated with EDS. Optimal conditions indicated composite with a content of 5% volume of graphite with the lowest porosity level of 5.10%, the relative density of 88.67%, and the highest hardness at 50.37 HV, followed by superior wear resistance. This result is supported by observing secondary phase formation and microstructure through XRD and SEM-EDS. The addition of graphite in the composite supports optimizing physical and mechanical properties as a lightweight composite. It deserves to be considered as an alternative material for automotive components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".