Impact of Chromium Addition on the UTS and ETF of Aluminium Alloy AA6061 Chips Based Composite
Bibliographic record
Abstract
Aluminium matrix composite has been increasingly developed due to the impressive performances demonstrated, mechanically and physically. The related properties were enhanced with the addition of reinforcing materials and this tailor-made composite can be used in the area of automotive, aerospace and military. In this study, the matrix composite was developed directly from aluminium chips without remelting through a series of hot press forging operations. This process involved heating the aluminium chip mixtures with 1 to 8 wt% chromium content above the recrystallization temperature. Then, the uniaxial force was subsequently applied onto the composite. For performance measures, the hot pressed composites were tested for the ultimate tensile strength (UTS) and elongation to failure (ETF). The composite with 2 wt % of chromium exhibits significant improvement compared to other samples, with the UTS and ETF reached to 215.37 MPa and 30.65% respectively. This sample exceeds the minimum stress and strain of stock aluminium AA6061-T4. Furthermore, the addition of chromium beyond 2 wt % would generally cause reduction of the mechanical properties of the 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".