Vision-based deformation and wrinkle detection for semi-finished fiber products on curved surfaces
Why this work is in the frame
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Bibliographic record
Abstract
The paper focuses on a vision-based approach for optimizing automated deformation and draping processes of dry semi-finished fiber products at the production of large-area composite components for the aerospace industry. The vision-based approach developed at University of British Columbia, is to be utilized with the existing draping process, carried out on a form-variable end-effector, developed at the Center for Lightweight Production Technologies (ZLP) in Augsburg. During the deformation of the semi-finished product, tensions develop in the material leading to shearing and relative movements of the fiber material on the gripping surface. In turn, the resulting displacement and deformation of the cut piece negatively influences the production quality. The method proposed in the paper is designed to help in visually detecting and automatically evaluating the drape and deformation of the cut piece on a laboratory scale setup. For this purpose, RGB-D camera data is used to detect the deformed gripper surface and determine the position, the boundary geometry and any wrinkles that may have occurred in the cut piece. The accuracy of the proposed method is verified by experiments on a known target geometry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it