Vision-based deformation and wrinkle detection for semi-finished fiber products on curved surfaces
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".