Workflows For X-ray And Neutron Interferometry/Tomography As Applied To Additive Manufacturing
Bibliographic record
Abstract
Grating-based interferometry/tomography is being rapidly developed for non-destructive evaluation of additive manufacturing test articles. An application requiring an efficient workflow is extremely necessary for stress and fatigue testing samples. At present, scientific workflows play an important role for computational experiments in additive manufacturing 3D printing and interferometry/tomography imaging analysis. A clear workflow template allows scientists to process experiments easier and faster. Work flow library grows, but to find an appropriate workflow for their task is challenging. In our research, there are mainly three portions in the workflow, interferometry analysis, image reconstruction and 3D visualization. Currently, the hierarchy of workflows in interferom etry/tomography projects is Mathematica, TomoPy/ASTRA/Jupyter notebook, VisTrails and Dragonfly. In general, two methods of interferometry analysis have been used in the first portion of workflow, single-shot interferometry and stepped-grating interferometry. As for the second portion, with a Jupyter notebook, the reconstruction method ’Gridrec’ in TomoPy and ’SIRT’ (Simultaneous Iterative Reconstruction Technique) in ASTRA gener ated a powerful reconstruction volume for absorption projections and dark-field projections separately. For the last portion, Dragonfly developed by ORS (Object Research System) company is a 3D visualization software with powerful scripting capabilities implemented in Python macros. Meanwhile, the VisTrails workflow incorporated both interferometry anal ysis and image reconstruction portions into VisTrails modules. Workflows in VisTrails hide much of the complexity of Mathematica or Python programming from users. Instead, with a simple GUI, it is possible for users to make their interferometry/tomography workflows through VisTrails modules. Especially, for DPC (differential phase contrast) images in grating-based interferome try/tomography, we addressed the phase unwrapping issue with the method of 2D integra tion through generating phase images. With the algorithm, we have demonstrated the 2D integrated phase images denote a clearer contrast than DPC images.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.055 | 0.052 |
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".