CAD-based x-ray CT calibration and error compensation
Bibliographic record
Abstract
Abstract Artifacts due to imperfect determination of the scanner geometry, beam hardening (BH), and diffuse Compton scattering, limit the quantitative exploitation of radiographs or tomographies for non-destructive evaluation. Exploiting the computer-aided design (CAD) model of an industrial part, a methodology is proposed to refine the estimation of the computed tomography (CT)-scanner geometry up to a scale factor, to correct or account for artifacts, and to assess the metrology of the part. A projective model describing the formation of x-ray images in CT-scanners is first introduced. The optimal parameters of the projective model are identified using a novel CAD-based calibration method that relies on the registration of simulated projections onto experimental ones. A metrological analysis based on the comparison between acquired and simulated x-ray images is proposed. A turbine blade, for which an automatic inspection procedure from few views is under development, is used as an example to illustrate the proposed methodology. The parametrization accounts for the refinement of the projection geometry, the calibration of BH, and the estimation of scattering. It is shown that, using the proposed procedure, the differences between acquired and simulated radiographic images are significantly reduced, indicating that the optimal parameters are properly identified. These differences are then exploited to detect flaws in the part.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".