Laser Powder Bed Fusion of Superelastic Ti-Ni Lattice Structures: Process Design and Testing
Bibliographic record
Abstract
Laser powder bed fusion allows the production of complex geometries and eases the shaping of difficult-to-transform materials, such as near-equiatomic Ti-Ni shape memory alloys. In this study, a numerical model was used to select 11 sets of printing parameters with different volumetric energy densities (VEDs) and build rates (BRs) to produce bulk Ti-50.26at%Ni alloy specimens. The manufactured specimens were studied in terms of their structural integrity, printed density, chemical composition, transformation temperatures, and crystalline phases. At high VEDs and low BRs, a significant decrease in the nickel content was observed. VED = 90 J/mm3 and BR = 10 cm3/h yielded a printed density of 99.94% and an austenite finish temperature of Af = 26.3 °C. The same printing conditions were used to produce 60% porous diamond and gyroid lattice structures. After heat treatment at 500 °C for 30 min, the diamond lattices manifested larger apparent recovery strains (7 vs. 6%), higher compliance (2.9 vs. 3.4 GPa), and similar yield stresses (~48 MPa) compared to their gyroid equivalents. The numerical model predicted that at an equivalent apparent compression strain of 6%, only a ~2% volume fraction of the diamond lattice material underwent plastic deformation as compared to ~20% for its gyroid equivalent.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".