Robotic Arm for Automated Assembly of Proton Exchange Membrane Fuel Cell Stacks
Bibliographic record
Abstract
We present an innovative, inexpensive end-effector, the robot workcell, and the fuel cell components used to demonstrate the automated assembly process of a proton exchange membrane fuel cell stack. The end-effector is capable of handling a variety of fuel cell components including membrane electrode assemblies, bipolar plates and gaskets using vacuum cups mounted on level compensators and connected to a miniature vacuum pump. The end-effector and the fuel cell components are designed with features that allow an accurate component alignment during the assembly process within a tolerance of 0.02 in. and avoiding component overlapping which represents a major cause of overboard gas leaks during the fuel cell operation. The accurate component alignment in the stack is achieved with electrically nonconductive alignment pins permanently mounted on one fuel cell endplate and positioning holes machined on the fuel cell components and on the end-effector. The alignment pins feature a conical tip which eases the engagement between them and the positioning holes. A passive compliance system consisting of two perpendicularly mounted miniature linear blocks and rails allow compensating for the robot's limitations in accuracy and repeatability.
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".