Motion Compensation for Industrial Computed Tomography
Bibliographic record
Abstract
X-ray computed tomography (CT) is a powerful tool for industrial inspection. However, the harsh conditions encountered in some production environments make accurate motion control difficult, leading to motion artifacts in CT applications. A technique is demonstrated that removes motion artifacts by using an iterative-solver CT reconstruction method that includes a bulk Radon transform shifting step to align radiographic data before reconstruction. The paper uses log scanning in a sawmill as an example application. We show how for a known nominal object density distribution (circular prismatic in the case of a log), the geometric center and radius of the log may be approximated from its radiographs and any motion compensated for. This may then be fed into a previously developed iterative reconstruction CT scheme based on a polar voxel geometry and useful for describing logs. The method is validated by taking the known density distribution of a physical phantom and producing synthetic radiographs in which the axis of object rotation does not coincide with the center of field of view for a hypothetical scanner geometry. Reconstructions could then be made on radiographs that had been corrected and compared to those that had not. This was done for progressively larger offsets between these two axes and the reduction in voxel density vector error studied. For CT applications in industrial settings in which precise motion control is impractical or too costly, radiographic data shifting and scaling based on predictive models for the Radon transform appears to be a simple but effective technique.
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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 | 0.002 |
| 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) | 0.002 | 0.001 |
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".