DESIGN OF A ROBOTIC ARM FOR TEACHING INTEGRATED DESIGN
Bibliographic record
Abstract
Automation systems are generally made upof three main subsystems, namely mechanical, electricaland software. The interactions among these componentsaffect the integrated system in terms of reliability, quality,scalability, and cost. Therefore, it is imperative that thethree components of automation systems are designedconcurrently through an integrated design paradigm.This leads to the need to teach integrated design conceptsto students in programs such as process automation,electrical and computer engineering, and mechanicalengineering. However, due to the time constraint, it isalmost impossible to run full integrated design classprojects. Therefore, instructors have to decide on theparts of the design process that their class projects haveto focus on, and the parts that have to be reviewed for thecompleteness of the integrated design process. In thispaper we present the design and implementation of amicrocontroller based, 3D printable, low cost robotic armsuitable for teaching integrated design. Moreover, thepaper presents how the robotic arm design is used in anintegrated design project of an Industrial Networks andControllers course. Since the focus of this course is theelectrical and software subsystems of the robotic arm,and we do not have enough time to do a full design,students review the design of the robotic arm presented inthis paper and use it to either 3D print the robotic arm orpurchase the mechanical subsystem of the robotic armthat meets the specification.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".