Meta-structure of amorphous-inspired 65.1Co28.2Cr5.3Mo lattices augmented by artificial intelligence
Bibliographic record
Abstract
A hatching-distance-controlled lattice of 65.1Co28.2Cr5.3Mo is additively manufactured via laser powder bed fusion with a couple of periodic and aperiodic arrangements of nodes and struts. Thus, the proposed lattice has an amorphous-inspired structure in the short- and long-range orders. From the structural perspective, an artificial intelligence algorithm is used to effectively align lattices with various hatching distances. Then, the metastable lattice combination exhibits an unexpectedly high specific compression strength that is only slightly below that of a solid structure. From the microstructural perspective, the nodes in the newly designed lattice, where the thermal energy from laser irradiation is mainly concentrated, exhibit an equiaxial microstructure. By contrast, the struts exhibit a columnar microstructure, thereby allowing the thermal energy to pass through the narrow ligaments. The heterogeneous phase differences between the nodal and strut areas explain the strength-deteriorating mechanism, owing to the undesirable multi-phase development in the as-built state. However, solid-solution heat treatment to form a homogeneous phase bestows even higher specific compression strength. Furthermore, electrochemical leaching leads to the formation of nanovesicles on the surface of the microporous lattice system, thereby leading to a large surface area. A more advanced valve cage for use in a power plant is designed by using artificial intelligence both to (i) effectively preserve its mechanical stiffness and (ii) actively dissipate the generated stress through the large surface area provided by the nanovesicles. Supplementary Information: The online version contains supplementary material available at 10.1007/s42114-024-01039-6.
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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 | 0.001 | 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".