A Soft Needle Gripper Capable of Grasping and Piercing for Handling Food Materials
Bibliographic record
Abstract
Automation in the food industry is not as developed as in the automotive industry because of difficulties in handling food products with large variations in shape, size, and mechanical properties. In this paper, a pneumatic-driven soft needle gripper is proposed for handling food materials. It was constructed using a soft membrane and multiple rigid needle-like fingers. It can work under two operational modes: grasping and piercing. The grasping mode can be used to grasp shredded and chopped food materials. The piercing mode is for handling food products when only the top surface of the food product is available for handling. The needle gripper is fabricated using a multi-material 3D printer. Experimental tests on various food materials are conducted to validate grasping and piercing operations. The results of grasping tests suggest that the needle gripper can successfully grasp shredded and chopped food materials. A quantitative analysis shows that the relative standard deviation of the grasped food weight was within 20%. Although the results of piercing tests validate that the needle gripper can successfully handle various food materials, releasing the food materials involves certain difficulties.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".