Laser Remelting of a CrMnFeCoNi High‐Entropy Alloy: Effect of Energy Density on Elemental Segregation
Bibliographic record
Abstract
Laser remelting applies a high‐energy laser beam to the surface of a solidified metal or previously deposited layer, offering a rapid alternative to conventional heat treatments for homogenizing high‐entropy alloys (HEAs). However, the mechanisms governing elemental segregation in HEAs during laser remelting are not well understood. In this study, a CrMnFeCoNi, or the Cantor alloy, is prepared via arc melting in both as‐cast and annealed conditions and subjected to six distinct laser remelting energy densities to investigate the origins of chemical inhomogeneity. Two dominant phenomena are identified: 1) at high energy densities, Mn segregates to interdendritic regions due to reduced cooling rates and solute pile‐up; 2) at low energy densities, incomplete melt pool mixing preserves prior inhomogeneity, particularly in as‐cast substrates. An optimal processing window is identified below 50 J·mm−2, with effective homogenization occurring above 24 J·mm−2 for annealed and above 30 J·mm−2 for as‐cast samples. These results provide mechanistic insight into segregation during laser remelting and demonstrate its viability as a fast, localized alternative to conventional post‐process heat treatments for achieving chemical uniformity in HEAs.
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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".