3D Printed Structures for Under Water Robots Design
Bibliographic record
Abstract
Nowadays, with the continuous development of low-cost technologies such as 3D printing and open source hardware and software, the cost of building an ROV has been further reduced.This is why the present research aims to analyse the calculations necessary for the development of an ROV prior to its construction, obtaining significant improvements in design as well as a reduction of time and costs.This paper shows a comparison of the design parameters of a 3 DOF robot with 4 turbines and a 5 DOF robot with 6 turbines, to demonstrate the importance of CAD and CFD in underwater robots' design.The selection of actuators is based on the results of CFD, obtaining linear and quadratic, turbine rpm and the friction coefficient to determine the stability of the robot.A reduction in time and costs was obtained through CFD analysis prior to robot construction.The comparison between the open and close structures is evident that the close structure design in this paper has more stability and is better option for underwater robots.3D printing is a good alternative for underwater robots, the infill should be 100% to avoid leaks and breaks based on the stress test.The mayor disadvantage of 3D printing is the manufacturing time.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".