Effect of high iron content on direct recycling of unhomogenized aluminum 6063 scrap by Shear Assisted Processing and Extrusion
Bibliographic record
Abstract
Iron contamination in secondary aluminum scrap must be diluted with primary aluminum during conventional recycling to bring subsequent alloys within allowable Fe limits. In this study, Shear Assisted Processing and Extrusion (ShAPE) was investigated as a methodology for tolerating high Fe content during extrusion of secondary aluminum scrap. ShAPE was used to fabricate aluminum alloy 6063 tubing from secondary industrial scrap billets in the as-cast, unhomogenized condition. Iron content up to 0.3 wt% was investigated to explore the tolerance of ShAPE to ferrous contamination in the feedstock. Extensive refinement of Fe-rich second phases enabled tensile properties to meet, and in some cases exceed, industry standard values. For process temperatures of 470–530 °C, using unhomogenized billets with 0.3 wt% Fe and T6 properties yielded an average of 214 MPa yield strength, 243 MPa ultimate strength, and 15.5 % uniform elongation. For 0.2 wt% Fe and T6 condition, properties averaged 233 MPa yield strength, 260 MPa ultimate strength, and 16.5 % uniform elongation. These results suggest that ShAPE is a potential manufacturing route that can avoid the need for adding primary aluminum to dilute Fe during recycling.
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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".