A Component-Based Approach to Automated Flexible Material Handling: Interactive Compliant Gripper Design Relative to Mold Surfaces
Bibliographic record
Abstract
The challenges posed by flexible limp fabric material handling within automated manufacturing processes are addressed in this paper.With lightweight materials, particularly fiber composites, gaining prominence across industries, the need for effective and precise fabric placements is deemed crucial.Traditional gripping methods are often found to be lacking in addressing the complexities of fabric draping, necessitating innovative solutions.The deformable, delicate, and complex nature of fabric materials, combined with intricate mold surface geometries, presents significant hurdles for achieving wrinkle-free, accurate placements.To overcome these challenges, a compliant gripper approach is proposed.Associating surface geometry analysis with gripper design, the study aims to enhance the efficiency, precision, and adaptability of fabric placement in automated manufacturing settings.The study exploits the deformable robotic gripper concepts.Compliant mechanisms include variants inspired by the Miura-fold origami approach.The facet-based representation and data processing technique used for a mold's surface allows for associations between the design parameters of the compliant grippers and the mold surface geometry.Design guidelines encompass considerations related to geometry, structural integration, surface friction enhancement, and material selection to provide a baseline framework for the interactive design of compliant grippers.Case studies highlight the merits of the approach for different surface geometries and show the potential for compliant mechanisms being employed in flexible component automation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".