Liquid Metallurgy Synthesis and Thermo-Electrical Characterization of Copper-Aluminum Metal Matrix Composite
Bibliographic record
Abstract
Samples of copper-aluminum reinforced metal matrix composite (MMC) were prepared by liquid metallurgy method using micron–sized silicon carbide (SiC) particulate. The resultant MMC samples were characterized to determine their thermal, electrical and mechanical properties in respect to varying particle sizes (212, 425, 710 and 1200 mm) of the SiC. The analyses of the results obtained showed that the thermal conductivity of the composite increased with decrease in particle size and volume fraction of the SiC. Also with decrease in grains size, high thermal conductivity was achieved by increasing the volume fraction. The results obtained in this study showed that alloying Cu matrix with aluminium is effective in reducing the interfacial reactions of a typical Cu-SiC matrix composite. The synthesized MMC samples also possessed a combination of high thermal and electrical conductivities with a low coefficient of thermal expansions which is synonymous to a low tensile strain at a maximum load. These properties were achieved for a 60%Cu/Al(40%SiC) at 212 µm, 50%Cu/Al(50%SiC at 12 µm and 70%Cu/Al(30%SiC) at 710 µm. The microstructural evaluation using optical microscopy (OM) indicated good dispersion of the SiC particles in all the samples which consequently enhanced the microhardness obtained in the MMC samples.
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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".