Deformation Behavior of 3D‐Printed High‐Entropy Alloys: A Critical Review
Bibliographic record
Abstract
The 3D‐printing of high‐entropy alloys (HEAs) is capable of enhancing the design and manufacturing flexibilities for novel materials. Owing to the rapid solidification, the 3D‐printed HEAs exhibit markedly different microstructures than their conventionally‐manufactured equivalents, leading to peculiar mechanical properties. Many additively manufactured HEAs also break down the strength‐ductility trade‐off dilemma. Novel dynamics regarding the simultaneous and sequential nature of deformation mechanisms are emerging through recent intensive research on these materials. Herein, the deformation behavior of 3D‐printed HEAs is reviewed and explained on the basis of the latest advances in this area. A comprehensive picture regarding the role of cellular dislocation networks, twinning‐induced plasticity, and transformation‐induced plasticity in the monotonic and cyclic deformation of 3D‐printed HEAs is presented. Special emphasis is placed on fatigue characteristics due to the enormous interest in this burgeoning area. The effect of postfabrication thermomechanical processing on plasticity is discussed along with the microstructural evolution in the 3D‐printed HEAs. Several innovative developments that carry latent potential for further research are also deliberated in this review. Finally, the present understanding of the deformation behavior of 3D‐printed HEAs is summarized while gauging the future directions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".