A multi-technique tomography-based approach for non-invasive characterization of additive manufacturing components in view of vacuum/UHV applications: preliminary results
Bibliographic record
Abstract
Abstract In this paper, we have studied an additively manufactured metallic component, intended for ultra-high vacuum application, the exit-snout of the MACHINA transportable proton accelerator beam-line. Metal additive manufacturing components can exhibit heterogeneous and anisotropic microstructures. Two non-destructive imaging techniques, X-ray computed tomography and Neutron Tomography, were employed to examine its microstructure. They unveiled the presence of porosity and channels, the size and composition of grains and intergranular precipitates, and the general behavior of the spatial distribution of the solidification lines. While X-ray computed tomography evidenced qualitative details about the surface roughness and internal defects, neutron tomography showed excellent ability in imaging the spatial density distribution within the component. The anisotropy of the density was attributed to the material building orientation during the 3D printing process. Density variations suggest the possibility of defect pathways, which could affect high vacuum performances. In addition, these results highlight the importance of considering building orientation in the design for additive manufacturing for UHV applications. Graphical Abstract
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".