Mechatronic Design of Two-Jaw Pleating End-Effector for Large-Scale Carbon Fibre Reinforced Polymer Manufacturing
Bibliographic record
Abstract
Large-scale composite manufacturing processes, particularly vacuum injection moulding, are often hampered by expensive labour costs and low-levels of automation. The current process of manufacturing carbon fibre reinforced polymers (CFRP) for airplane fuselage workpieces requires experienced workers to spend hours manually placing, pleating and migrating multiple layers of fabrics in order to produce the desired composite part. A promising solution is to utilize robot platforms as assistants to alleviate the physical demands of the tasks. In this paper, we present the design and evaluate the performance of our novel Vacuum Infusion Moulding End-Effector (VIMEE) prototype in the pleating procedure of a simulated CFRP manufacturing vacuum bagging process, where our gripper system performs pleat loading, height detection and migration tasks. Experimental results show successful manipulation of nylon pleats by our mechatronic systems design. These results are promising, demonstrating our VIMEE design's potential value in vacuum injection moulding.
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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".