Optimum Accuracy of Two-Dimensional Strain Measurements Using Digital Image Correlation
Bibliographic record
Abstract
Foil and vibrating wire strain gauges have an optimum strain measurement accuracy of one microstrain. However, they can only provide discrete strain readings over a single fixed-gauge length. Digital image correlation (DIC) offers an alternative to conventional strain gauges because a two-dimensional (2D) surface strain field can be obtained from a single sensor (camera). However, the benefits of 2D strain measurements are only worthwhile if a similar level of measurement accuracy to conventional strain gauges can be achieved. This paper presents the results of an investigation into the optimum strain measurement accuracy that can be achieved by using the 2D technique on artificial images (which eliminate errors associated with cameras and lighting). The principle of the 2D DIC technique and its historical development will be introduced. Then, three potential techniques for taking strain measurements will be presented and compared: single readings, averaged linear readings, and an approach on the basis of Mohr’s circle. The Mohr’s circle approach was found to be the most accurate and was not susceptible to image misalignment. Strain measurement accuracy was also found to be affected by the bias error of the subpixel interpolation scheme, but the use of an 8 coefficient B-spline was found to produce satisfactory results within the error of conventional strain gauges. Gauge length was also found to have a significant effect on strain measurement accuracy, indicating that measuring strains in a material in which there are variations across the strain field could result in a loss of measurement accuracy. However, overall it was found that 2D DIC offers the same strain measurement accuracy as conventional strain gauges when used under ideal conditions.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".