The influence of elevated Fe and Zn impurities on the rapid solidification behaviour of AA6061 processed using single-track laser surface melting
Bibliographic record
Abstract
Increased adoption of recycled aluminum (Al) alloys in the automotive sector can provide several economic and environmental benefits through vehicle lightweighting, decreased fuel consumption, and reduction in greenhouse gas emissions. A major challenge in the adoption of secondary Al for a broader range of products is the accumulation of impurity elements, as increased scrap use can result in the compositional drift of alloy streams, leading to degraded mechanical and electrochemical properties. The objective of the current study is to demonstrate the use of rapid solidification processing (RSP) to increase the potential adoption of recycled Al through refinement of microstructural features and reduction of cracking. Cast ingots of an Al alloy 6061 (AA6061) were produced with iron (Fe) and zinc (Zn) additions in amounts ranging from 0 to 1 wt% to simulate recycling impurities. Thermodynamic simulations were used to predict the crack susceptibility of each alloy composition. Laser surface melting (LSM) trials were performed on plates cut from each ingot to generate rapidly solidified microstructures. The simulation predictions and microstructure results suggest that alloy impurity composition does influence the cracking behaviour observed in the laser melt pools, with both Fe and Zn additions having a mitigating effect on the observed cracking behaviour. The results suggest that the adoption of techniques such as additive manufacturing and laser welding could enable greater use of recycled Al alloys, advancing their use for automotive applications.
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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".