3D strain mapping inside materials by microstructural tracking in tomographic volumes
Bibliographic record
Abstract
X-ray absorption microtomography has been employed to trace the physical displacement of internal microstructural features in order to obtain local strain distribution inside materials. The number of microstructural features visible by means of state-of-the-art synchrotron radiation microtomography sometimes reaches into the tens of thousands or more in ordinary structural materials. It implies that high-density strain mapping is enabled if such large-scale tracking is accurately performed. The present paper describes a method to accurately track microstructural features by utilising the information on the size, shape and gravity centre of microstructural features together with the spring model particle tracking algorithm and exploratory registration using macroscopic deformation pattern. A model material which contains artificially introduced micro-pores has been prepared and used for the investigation. It has been clarified that almost perfect tracking is realised if the procedure is adequately applied to 3D image data sets. 3D internal strain mapping is also demonstrated and correlated to the localised ductile fracture of the model material.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".