Control of Density and Grain Structure of a Laser Powder Bed-Fused Superelastic Ti-18Zr-14Nb Alloy: Simulation-Driven Process Mapping
Bibliographic record
Abstract
This study focuses on the control of density and grain structure of a superelastic Ti-18Zr-14Nb (at. %) alloy subjected to laser powder bed fusion. It starts with the production and characterization of a Ti-18Zr-14Nb powder feedstock and printing of a series of calibration specimens. These specimens are next subjected to chemical, structural, phase and texture analyses in order to collect experimental data needed to build simulation-driven processing maps in the laser energy density–material build rate coordinates. The results of this study prove that, once calibrated, the simulation-driven processing maps can be used to relate the main LPBF parameters (laser power, scanning speed, hatching distance and layer thickness) to the density and grain structure of the printed material, and the process productivity (build rate). Even though this demonstration is made for a specific material–system combination (TiNbZr & TruPrint 1000), such a process mapping is feasible for any material–system combination and can, therefore, be exploited for the process optimization purposes and for manufacturing of functionally graded materials or parts with intentionally seeded porosity.
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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".