Design and Development of a Non-Contact Bernoulli Gripper
Bibliographic record
Abstract
In the industry, the conventional approach to manipulating objects typically involves suction-based methods utilizing vacuum technology.However, this research project introduces an innovative end-effector prototype rooted in Bernoulli's principle.By harnessing high-velocity airflow, this prototype generates suction for non-contact gripping of objects.This proposes a solution aimed at handling objects without direct contact, thereby enhancing automated handling processes, particularly in sectors such as food, electronics, textiles, and others where product sensitivity to damage necessitates alternative handling methods.The final end-effector design prioritizes secure and precise object manipulation without compromising product quality.Leveraging CAD design software, we developed a 3D prototype fabricated using PLA material, ensuring functionality at a low cost.Critical to the effector's design was the analysis of pressure distribution.Through SolidWorks Flow simulation, we obtained real-time insights into compressed airflow behavior within the prototype, including trajectory dynamics.Based on the results obtained, where the maximum weight lifted was 76 grams, we can deduce that our effector is capable of lifting approximately 0.0105 grams per square millimeter (g/mm²) at an air pressure of 110 psi.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".