Effect of fly ash particles on the mechanical properties of aluminium casting alloy A535
Bibliographic record
Abstract
Fly ash is a lightweight coal combustion byproduct (CCB) which is separated from the exhaust gases of power generating plants using suspension fired furnaces in which pulverised coal is used as the fuel. Its physical and chemical properties make it a useful construction and industrial material, especially in cement manufacturing, concrete, liquid waste stabilisation, and hydraulic mine backfill. The addition of fly ash into aluminium alloys has the potential to reduce the cost and density of aluminium castings while improving other physical and mechanical properties of the resulting metal matrix composites (MMCs). In the present study, the effect of fly ash addition on the mechanical properties of aluminium casting alloy 535 (A535) was investigated by means of hardness measurements, tensile testing, Charpy impact testing, optical microscopy, scanning electron microscopy (SEM), energy dispersive X-ray spectrometry (EDS), and X-ray fluorescence spectroscopy (XFS). The unreinforced A535 and its MMCs containing a mixture of 5 wt-% fly ash and 5 wt-% silicon carbide, 10 wt-% fly ash and 15 wt-% fly ash were investigated in the as cast and heat treated conditions. The results show that increasing the fly ash content of the melt increased the porosity of the castings, which ultimately affected the tensile and impact properties of the MMCs. The microhardness, tensile strength and Charpy impact energy of the composites decreased with increasing fly ash content. The loss in mechanical properties and impact resistance of the MMCs is attributed partly to the depletion of solid solution strengthening magnesium atoms from the matrix and partly to porosity.
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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".