Consolidation of 2124 Aluminum Alloy – Carbon Nanotube Reinforced Metal Matrix Composites
Bibliographic record
Abstract
Powder metallurgy techniques have emerged as a promising way for the fabrication of nanotube reinforced metal matrix composites. The present study reports on the investigation of the optimum consolidation conditions that give the best mechanical properties, for different carbon nanotubes weight percentages and extrusion ratios, while minimizing any chemical interfacial reactions between the matrix and the reinforcements. The conditions investigated were hot compaction followed by hot extrusion, cold compaction followed by hot extrusion, and hot compaction followed by cold extrusion . The tensile behaviors for samples of the three processing conditions were evaluated and compared. An increase in the yield strength of the carbon nanotubes CNT composites under most conditions were observed. The highest strength values for composites were obtained using the hot compaction/cold extrusion route at extrusion ratio 4:1, on the expense of the ductility. Better strength values were obtained for the hot compaction/hot extrusion route at higher extrusion ratio of 5:1 with restoring good ductility.
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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) | 0.000 | 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 | 0.001 |
| 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 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".