Measurement of 3-D Strain Distribution by means of High-Resolution X-ray CT Image and Tracking of Microstructural Features
Bibliographic record
Abstract
Measurement of interior strain distribution has been developed based on the synchrotron radiation computed tomography (SR-CT). In metal deformation, local strain distribution differs from macroscopic strain due to microstructural factors such as grains, grain boundaries, particles, pores, voids, and cracks. A model sample, which was made from a copper alloy strengthened with alumina, was prepared containing artificial pores. Tensile loading was applied to the sample step by step. High-resolution tomographic experiment was performed at the third-generation synchrotron radiation facility (SPring-8) in Japan. Gravity center position, volume and surface area in the pores, which were regarded as markers in a tracking procedure, were measured by 3-D digital image analysis in the SR-CT images. The markers before and after the deformation were provided for registration and macroscopic strain correction before the tracking procedure. The marker tracking was carried out by means of matching parameter that was described as functions of distance, volume and surface at markers. The ratio of success tracking was evaluated in order to clarify whether the tracking method developed in this study was reliable. The 3-D strain distribution was represented successfully by the tracking results. A combination of high-resolution SR-CT and tracking of microstructural features is effective to visualize interior strain distribution in materials in 3-D.
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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 | 0.000 | 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".