Visualising complex morphology of fatigue cracks in voxel based 3D datasets
Bibliographic record
Abstract
Fatigue cracks are usually characterised by surface sensitive techniques after specimen failure. High resolution micro computed tomography (μCT) based on synchrotron radiation allows the non-destructive visualisation of crack morphology and evaluation of fatigue crack formation/propagation before specimen failure. The visualisation of the complex fracture morphology with characteristic features out of the acquired set of slices is, however, challenging. To obtain a reasonable estimate, two approaches are generally used: the determination of mass centre points in the hollow space and the minimum intensity search in parallel projections. The more sophisticated approach using the elastically deformable contour model, the physical analogy of a rubber band, termed snakes, gives rise to crack morphologies with much less artefacts. The approach was used in the present study for the characterisation of fatigue cracks in poly(methylmethacrylate) (PMMA) and a dental ceramic. The search for the appropriate snake parameters works much better for homogeneous materials, here PMMA, than for inhomogeneous materials, here a dental ceramic. For the ceramic, the regions where the snakes approach provided reasonable results were restricted. Combining μCT with sophisticated computer vision techniques enables the unique characterisation of cracks at the micrometre scale.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".