Digital Image Correlation System Application - Measuring Deformation and Load of Convertible Top Fabric
Bibliographic record
Abstract
Strain gages have been widely used for measuring strain or deformation. They are very reliable and accurate. However, for application on fabric material, strain gages have their limitations. In this paper, digital image correlation (DIC) is used to measure the deformation around the rear window on a convertible top. The test needed to be non destructive, the vehicle and convertible top could not be damaged. The deformation or strain measured on the fabric was used to estimate the force experienced at the interface between the glass and the fabric during an opening/closing application. A speckle pattern was created on the convertible fabric where deformation was to be measured with washable paint. The image of the measured area was first recorded. The convertible top was then latched down and the fabric was stretched. A second image was recorded again. Based on the two images, the deformation/strain between the two conditions was measured. Convertible top fabric from a sample batch was cut into 50 × 200 mm coupon strips. Using a tensile test, a relationship between the applied force and the deformation on the strip was established. The relationship was used to estimate the force on the fabric over a 50 mm width. The application and the difficulties encountered during the measurements are discussed in this paper. The specific numerical results are limited to this particular convertible top.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".