Projection speckle digital correlation for surface out-of-plane deformation measurement
Bibliographic record
Abstract
The paper presents a new study on the method of Projection Speckle Digital Correlation (PSDC) for surface out-of-plane displacement and tilt measurement. Considering that perspective and parallel devices differ substantially in the nature of pattern projection and imaging, four different camera-projector setups are modeled by optical triangulation. The different W-u relationships that the models give indicate the impact of the device properties on raw measurement. In assessing overall error sources and error structure in the PSDC measurement, sources and magnitudes of the error in relation to Digital Speckle Correlation (DSC) are evaluated since DSC is a core technique embedded in the P SDC for image in-plane motion extraction. Another category of the errors inherent to the PSDC is analyzed, which is due to the misuse of the field equations. For a particular PSDC setup, such systematic error is correctable by a calibration test using a planar sample with known rigid-body motion. A case application serves as a demonstration of the potential of the low cost system, in which DSC and PSDC are combined to resolve 3D deformation in a 1 mm2area in a notched tensile sample.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".