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Workflows For X-ray And Neutron Interferometry/Tomography As Applied To Additive Manufacturing

2018· dissertation· en· W2804843768 on OpenAlexfundno aff
Jumao Yuan

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicAdvanced X-ray Imaging Techniques
Canadian institutionsnot available
FundersYork University
KeywordsInterferometryWorkflowX-rayTomographyMaterials scienceComputer scienceMedical physicsOpticsMechanical engineeringNuclear engineeringEngineering drawingEngineeringPhysics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.006
GPT teacher head0.279
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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