The Practicality of Quality Assessment Metrics for Millimetre‐Scale Digital Image Correlation Speckle Patterns
Bibliographic record
Abstract
ABSTRACT Digital image correlation (DIC), often used in multidisciplinary applications by nonexperts, relies heavily on the quality of a speckle pattern for accurate deformation tracking. Numerous metrics have been developed to assess speckle pattern quality and guide decision‐making in DIC postprocessing. This investigation considered the applicability of these metrics in the selection of an optimal speckling method for two‐dimensional DIC strain measurements, using the standard Brazilian tensile strength rock mechanics laboratory test as a case study. Four speckle patterning methods—spray paint, airbrush, stamp and laser engraving—were optimized to produce patterns of good visual quality for this specific DIC setup. Twenty specimens, five for each method, were prepared, and their speckle patterns were analysed using 10 published metrics. Each pattern was also deformed numerically to quantify the associated systematic and random errors. Overall, none of the 10 metrics demonstrated substantial agreement with the numerical experiment errors due to their failure to account for the confounding influence of pattern characteristics. Consequently, these tools are limited in their usefulness to evaluate the accuracy and reproducibility of speckle patterns with similar characteristics. Numerical simulation of pattern deformation is recommended as the most accurate resource for nonexpert DIC users to assess reproducibility and select optimal patterning methods in practical applications.
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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 | 0.024 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".