Deformation mechanisms of an additively manufactured high-entropy alloy under cyclic loading
Bibliographic record
Abstract
• Multiple deformation mechanisms activate in cyclic plastic zone of 3D-printed HEA. • Grain rotation causes misorientation up to 30° from 〈001〉 pole to 〈101〉 pole. • Lattice rotation & twinning at the crack tip curves slip traces & twin boundaries. • Elongated grains subdivide into elastic/plastic regions, creating intragranular cracks. • Intrinsic and extrinsic toughening mechanisms co-exist, enhancing LCF resistance. Additive manufacturing, commonly known as 3D-printing, of high-entropy alloys (HEAs) has garnered significant attention for its ability to create intricate geometries and tailored microstructures. HEAs exhibit superior mechanical properties, including high strength, ductility, and resistance to wear, corrosion, and high temperatures, making them well-suited for use in demanding environments. However, the successful application of 3D-printed HEAs depends critically on understanding their deformation mechanisms under dynamic fatigue loading. This study delves into the microstructural features and deformation mechanisms of a CrMnFeCoNi HEA fabricated via laser-beam powder bed fusion, subjected to cyclic loading at a strain ratio of R = −1. Detailed electron backscatter diffraction (EBSD) analysis of microstructurally small cracks revealed significant grain rotation driven by the activation of multiple slip systems, with misorientations up to 30° in the reoriented regions, which rotated mainly from the 〈001〉 pole towards the 〈101〉 pole. Notably, curvilinear deformation twinning was identified ahead of the crack tip, while higher-lower strain boundaries within the elongated grains provided low-energy transgranular paths for crack propagation. Additionally, the localized plastic deformation on one side of the crack caused uneven displacement of crack surfaces, enhancing roughness-induced crack closure. The findings shed light on the deformation mechanisms of HEAs, paving the way for expanding their applications in cyclic load-bearing structures.
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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) | 0.002 | 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".