Turning and Drilling Machinability of Recycled Aluminum Alloys
Bibliographic record
Abstract
A great deal of effort and research has been dedicated to recycled aluminum alloys, mainly to recycling processes and to the mechanical properties of recomposed parts; however, very limited work has been oriented towards the machinability of recycled aluminum materials. Recycled and recomposed aluminum parts sometimes need machining to obtain the final usable part shape and for assembly purposes. The acceptability of using recycled materials in design and engineering applications depends not only on their mechanical properties, but also on their machinability. This paper investigates the machinability of recycled aluminum alloys based on surface finish, cutting forces and chip formation. Two recycled foundry aluminum alloys were used: one from aluminum can covers and another from aluminum chips produced during machining. The machining operations investigated included turning and drilling under dry and wet conditions. The two tested recycled aluminum alloys showed different machinability behaviors and different part quality characteristics, suggesting that it would be desirable to consider separating aluminum wastes and chips considered for recycling by origin or type prior to melting and recasting.
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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 | 0.001 |
| 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 | 0.000 |
| 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".