Removal of Iron and Manganese Impurities from Secondary Aluminum Melts Using Microstructural Engineering Techniques
Bibliographic record
Abstract
Aluminum alloys are known for their high strength-to-weight ratio, excellent castability, thermal & electrical conductivities, and outstanding corrosion resistance. This unique combination of properties enables aluminum alloys to be widely used in diverse industries such as automotive, aerospace, packaging, wiring & electrical cables, building & construction, and consumer electronics. Because of its high recyclability, aluminum is a material of choice for a circular economy. However, during recurring scrap recycling, aluminum recycling can be complicated by the gradual accumulation of unwanted impurities, mainly Fe, Mn, and other (Cu, Si, Zn, and Mg) alloying elements. The impurity pickup can result in significant economic devaluation of scrap where aluminum products are down-cycled to produce aluminum products with lower value and performance. Managing the impurity content of aluminum scrap melt is, therefore, very important. Using microstructural engineering techniques, impurity elements can be removed by cooling molten aluminum, resulting in the formation of intermetallic compounds containing only one or more impurity elements as solid inclusions. Subsequent removal of intermetallic sediments by decantation and filtration techniques can provide an economical method for removing the desired amount of impurities to meet the product specifications. To neutralize the negative effects of mainly Fe, alloying elements such as Mn and Cr can be used to modify the morphology of Fe intermetallic phases to a less harmful microstructure. Using microstructural engineering techniques, Fe, Mn, and Cr complexes could also remove Fe and Mn impurities from molten aluminum. The present study studied the simultaneous removal of Fe and Mn from low and high Si-containing Al-alloys via an intermetallics sedimentation route. Calculation of Phase Diagrams modeling (CALPHAD) was also carried out using ThermoCalc to determine the suitable temperature for impurity-rich intermetallic phase formation in experimental alloys. Effects of holding time and temperature were also examined for maximum impurity removal. This paper will also discuss the results obtained at the laboratory that could be useful in performing commercial-scale experiments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".